In-Call Experience Enhancement for Assistant Systems

ABSTRACT

In one embodiment, a method includes establishing a video call between a plurality of client systems, wherein access to an assistant system is persistently maintained during the video call, receiving, from a first client system of the plurality of client systems, a request by a first user to be performed by the assistant system during the video call, wherein the request references one or more activities associated with one or more users associated with the plurality of client systems, analyzing, by a context engine of the assistant system, images of a scene of the video call to identify the one or more activities within the scene, instructing the assistant system to execute the request based on the identified one or more activities, and sending, to one or more of the plurality of client systems, a response to the request while maintaining the video call between the plurality of client systems.

PRIORITY

This application is a continuation under 35 U.S.C. § 120 of U.S. patentapplication Ser. No. 16/847,155, filed 13 Apr. 2021, which claims thebenefit under 35 U.S.C. § 119(e) of U.S. Provisional Patent ApplicationNo. 62/923,342, filed 18 Oct. 2019, each of which is incorporated hereinby reference.

TECHNICAL FIELD

This disclosure generally relates to databases and file managementwithin network environments, and in particular relates to hardware andsoftware for smart assistant systems.

BACKGROUND

An assistant system can provide information or services on behalf of auser based on a combination of user input, location awareness, and theability to access information from a variety of online sources (such asweather conditions, traffic congestion, news, stock prices, userschedules, retail prices, etc.). The user input may include text (e.g.,online chat), especially in an instant messaging application or otherapplications, voice, images, motion, or a combination of them. Theassistant system may perform concierge-type services (e.g., makingdinner reservations, purchasing event tickets, making travelarrangements) or provide information based on the user input. Theassistant system may also perform management or data-handling tasksbased on online information and events without user initiation orinteraction. Examples of those tasks that may be performed by anassistant system may include schedule management (e.g., sending an alertto a dinner date that a user is running late due to traffic conditions,update schedules for both parties, and change the restaurant reservationtime). The assistant system may be enabled by the combination ofcomputing devices, application programming interfaces (APIs), and theproliferation of applications on user devices.

A social-networking system, which may include a social-networkingwebsite, may enable its users (such as persons or organizations) tointeract with it and with each other through it. The social-networkingsystem may, with input from a user, create and store in thesocial-networking system a user profile associated with the user. Theuser profile may include demographic information, communication-channelinformation, and information on personal interests of the user. Thesocial-networking system may also, with input from a user, create andstore a record of relationships of the user with other users of thesocial-networking system, as well as provide services (e.g. profile/newsfeed posts, photo-sharing, event organization, messaging, games, oradvertisements) to facilitate social interaction between or among users.

The social-networking system may send over one or more networks contentor messages related to its services to a mobile or other computingdevice of a user. A user may also install software applications on amobile or other computing device of the user for accessing a userprofile of the user and other data within the social-networking system.The social-networking system may generate a personalized set of contentobjects to display to a user, such as a newsfeed of aggregated storiesof other users connected to the user.

SUMMARY OF PARTICULAR EMBODIMENTS

In particular embodiments, the assistant system may assist a user toobtain information or services. The assistant system may enable the userto interact with it with multi-modal user input (such as voice, text,image, video, motion) in stateful and multi-turn conversations to getassistance. As an example and not by way of limitation, the assistantsystem may support both audio (verbal) input and nonverbal input, suchas vision, location, gesture, motion, or hybrid/multi-modal input. Theassistant system may create and store a user profile comprising bothpersonal and contextual information associated with the user. Inparticular embodiments, the assistant system may analyze the user inputusing natural-language understanding. The analysis may be based on theuser profile of the user for more personalized and context-awareunderstanding. The assistant system may resolve entities associated withthe user input based on the analysis. In particular embodiments, theassistant system may interact with different agents to obtaininformation or services that are associated with the resolved entities.The assistant system may generate a response for the user regarding theinformation or services by using natural-language generation. Throughthe interaction with the user, the assistant system may usedialog-management techniques to manage and advance the conversation flowwith the user. In particular embodiments, the assistant system mayfurther assist the user to effectively and efficiently digest theobtained information by summarizing the information. The assistantsystem may also assist the user to be more engaging with an onlinesocial network by providing tools that help the user interact with theonline social network (e.g., creating posts, comments, messages). Theassistant system may additionally assist the user to manage differenttasks such as keeping track of events. In particular embodiments, theassistant system may proactively execute, without a user input, tasksthat are relevant to user interests and preferences based on the userprofile, at a time relevant for the user. In particular embodiments, theassistant system may check privacy settings to ensure that accessing auser's profile or other user information and executing different tasksare permitted subject to the user's privacy settings.

In particular embodiments, the assistant system may assist the user viaa hybrid architecture built upon both client-side processes andserver-side processes. The client-side processes and the server-sideprocesses may be two parallel workflows for processing a user input andproviding assistance to the user. In particular embodiments, theclient-side processes may be performed locally on a client systemassociated with a user. By contrast, the server-side processes may beperformed remotely on one or more computing systems. In particularembodiments, an arbitrator on the client system may coordinate receivinguser input (e.g., an audio signal), determine whether to use aclient-side process, a server-side process, or both, to respond to theuser input, and analyze the processing results from each process. Thearbitrator may instruct agents on the client-side or server-side toexecute tasks associated with the user input based on the aforementionedanalyses. The execution results may be further rendered as output to theclient system. By leveraging both client-side and server-side processes,the assistant system can effectively assist a user with optimal usage ofcomputing resources while at the same time protecting user privacy andenhancing security.

In particular embodiment, an in-call experience enhancement in which theassistant system is persistently active, but on standby during a call(such as a video or audio call) or other communication session (such asa text message thread), is provided. Such a persistently activeassistant system may enable a user to invoke it in real-time during thecall to execute tasks related to one or more other users on the call.Furthermore, the persistently active assistant system may allow a singlecommunication domain to be used in which the user can communicate withboth other people via the call and with the assistant system itself.Current assistant systems typically go dormant during calls, so that auser must pause the call and reawaken the assistant system in order toissue commands. Thus, this single communication domain may greatlyimprove the user's experience, enabling a more social and naturalinteraction. The persistent assistant system may utilize an underlyingmultimodal architecture having separate context and scene understandingengines. The context engine may also be persistent during the call,gathering data for use by other modules in the assistant system thatresponds to a user query (subject to privacy settings). By contrast, thescene understanding engine may be awakened as needed to receive the datagathered by the context engine and determines a relationship amongdetected entities. Accordingly, with a video call in particular servingas a social experience backdrop, this persistent assistant system mayenable numerous social, utility, communication, and image processingfunctionalities to be performed.

In particular embodiments, a video call between a plurality of clientsystems may be established, while persistently maintaining access to anassistant system during the video call. A request to be performed by theassistant system during the video call may then be received from a firstclient system of a first user. This request may reference one or moresecond users associated with second client systems. An intent of therequest and one or more user identifiers of these one or more secondusers referenced by the request may be determined, and the assistantsystem may be instructed to execute the request based on the determinedintent and user identifiers. Finally, a response to the request may besent to one more of the plurality of client systems while maintainingthe video call between the plurality of client systems.

Certain technical challenges exist in maintaining a quality video callbetween users. Video calls may lack a feeling of genuine socialinteraction; providing more social functions that may be performedduring an actual video call may thus increase user interaction andsatisfaction with the video call. However, one technical challenge tothis may include identifying users in the video call that a viewing userin the video call wants to perform some social function with, as well asactually understanding the scene and context of the video call in orderto more accurately execute the social function. A solution presented byembodiments disclosed herein to address this challenge may thus includecontinuously gathering context of the video call via a context engineand feeding this gathered information into a scene understanding engine,in order to generate relationship information between people and objectsin the scene of the video call. Another technical challenge may be that,when conducting a video call on a client device, the user of that devicemay wish to preserve access to the functions of the device and access toa smart assistant system, which may go dormant during the video call. Asolution presented by embodiments disclosed herein to address thischallenge may thus involve a persistent assistant system that, ratherthan going dormant during a video call, remains active but on standby,and is thus accessible to the user to be invoked during a video call toexecute various commands.

Certain embodiments disclosed herein may provide one or more technicaladvantages. As an example, accurately identifying any users and objectsin a video call, as well as their context and relationship information(subject to privacy settings), may enable a viewing user to perform avariety of social functions with respect to entities in the video call,even when the viewing user communicates those functions ambiguously. Asanother example, providing a persistent, always-on assistant system mayenable a user to continue to use their client device and smart assistantnormally, even while conducting a video call. Certain embodimentsdisclosed herein may provide none, some, or all of the above technicaladvantages. One or more other technical advantages may be readilyapparent to one skilled in the art in view of the figures, descriptions,and claims of the present disclosure.

The embodiments disclosed herein are only examples, and the scope ofthis disclosure is not limited to them. Particular embodiments mayinclude all, some, or none of the components, elements, features,functions, operations, or steps of the embodiments disclosed herein.Embodiments according to the invention are in particular disclosed inthe attached claims directed to a method, a storage medium, a system anda computer program product, wherein any feature mentioned in one claimcategory, e.g. method, can be claimed in another claim category, e.g.system, as well. The dependencies or references back in the attachedclaims are chosen for formal reasons only. However, any subject matterresulting from a deliberate reference back to any previous claims (inparticular multiple dependencies) can be claimed as well, so that anycombination of claims and the features thereof are disclosed and can beclaimed regardless of the dependencies chosen in the attached claims.The subject-matter which can be claimed comprises not only thecombinations of features as set out in the attached claims but also anyother combination of features in the claims, wherein each featurementioned in the claims can be combined with any other feature orcombination of other features in the claims. Furthermore, any of theembodiments and features described or depicted herein can be claimed ina separate claim and/or in any combination with any embodiment orfeature described or depicted herein or with any of the features of theattached claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example network environment associated with anassistant system.

FIG. 2 illustrates an example architecture of the assistant system.

FIG. 3 illustrates an example flow diagram of server-side processes ofthe assistant system.

FIG. 4 illustrates an example flow diagram of processing a user input bythe assistant system.

FIG. 5 illustrates an example multimodal architecture of the assistantsystem.

FIG. 6A illustrates an example initial scene viewed during a video callon a first client system of a first user.

FIG. 6B illustrates an example chart of information of the scenegenerated by an always-on context engine.

FIG. 6C illustrates an example knowledge graph of the scene generated bya scene understanding engine.

FIG. 7A illustrates an example shifted scene viewed after a user commandconcerning an entity of the initial scene on the first client system ofthe first user.

FIG. 7B illustrates an example updated chart of information of theshifted scene generated by the context engine.

FIG. 7C illustrates an example updated knowledge graph of the shiftedscene generated by the scene understanding engine.

FIG. 8 illustrates an example updated scene viewed after a user commandconcerning an entity of a previous scene on the first client system ofthe first user.

FIG. 9 illustrates an example video call in which content relevant tothe video call is viewed on the client system of a user.

FIG. 10 illustrates an example method for generating a response to auser request to a persistent assistant system made during a call.

FIG. 11 illustrates an example social graph.

FIG. 12 illustrates an example view of an embedding space.

FIG. 13 illustrates an example artificial neural network.

FIG. 14 illustrates an example computer system.

DESCRIPTION OF EXAMPLE EMBODIMENTS System Overview

FIG. 1 illustrates an example network environment 100 associated with anassistant system. Network environment 100 includes a client system 130,an assistant system 140, a social-networking system 160, and athird-party system 170 connected to each other by a network 110.Although FIG. 1 illustrates a particular arrangement of a client system130, an assistant system 140, a social-networking system 160, athird-party system 170, and a network 110, this disclosure contemplatesany suitable arrangement of a client system 130, an assistant system140, a social-networking system 160, a third-party system 170, and anetwork 110. As an example and not by way of limitation, two or more ofa client system 130, a social-networking system 160, an assistant system140, and a third-party system 170 may be connected to each otherdirectly, bypassing a network 110. As another example, two or more of aclient system 130, an assistant system 140, a social-networking system160, and a third-party system 170 may be physically or logicallyco-located with each other in whole or in part. Moreover, although FIG.1 illustrates a particular number of client systems 130, assistantsystems 140, social-networking systems 160, third-party systems 170, andnetworks 110, this disclosure contemplates any suitable number of clientsystems 130, assistant systems 140, social-networking systems 160,third-party systems 170, and networks 110. As an example and not by wayof limitation, network environment 100 may include multiple clientsystems 130, assistant systems 140, social-networking systems 160,third-party systems 170, and networks 110.

This disclosure contemplates any suitable network 110. As an example andnot by way of limitation, one or more portions of a network 110 mayinclude an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local area network (LAN), a wireless LAN (WLAN), a widearea network (WAN), a wireless WAN (WWAN), a metropolitan area network(MAN), a portion of the Internet, a portion of the Public SwitchedTelephone Network (PSTN), a cellular telephone network, or a combinationof two or more of these. A network 110 may include one or more networks110.

Links 150 may connect a client system 130, an assistant system 140, asocial-networking system 160, and a third-party system 170 to acommunication network 110 or to each other. This disclosure contemplatesany suitable links 150. In particular embodiments, one or more links 150include one or more wireline (such as for example Digital SubscriberLine (DSL) or Data Over Cable Service Interface Specification (DOCSIS)),wireless (such as for example Wi-Fi or Worldwide Interoperability forMicrowave Access (WiMAX)), or optical (such as for example SynchronousOptical Network (SONET) or Synchronous Digital Hierarchy (SDH)) links.In particular embodiments, one or more links 150 each include an ad hocnetwork, an intranet, an extranet, a VPN, a LAN, a WLAN, a WAN, a WWAN,a MAN, a portion of the Internet, a portion of the PSTN, a cellulartechnology-based network, a satellite communications technology-basednetwork, another link 150, or a combination of two or more such links150. Links 150 need not necessarily be the same throughout a networkenvironment 100. One or more first links 150 may differ in one or morerespects from one or more second links 150.

In particular embodiments, a client system 130 may be an electronicdevice including hardware, software, or embedded logic components or acombination of two or more such components and capable of carrying outthe appropriate functionalities implemented or supported by a clientsystem 130. As an example and not by way of limitation, a client system130 may include a computer system such as a desktop computer, notebookor laptop computer, netbook, a tablet computer, e-book reader, GPSdevice, camera, personal digital assistant (PDA), handheld electronicdevice, cellular telephone, smartphone, smart speaker, virtual reality(VR) headset, augment reality (AR) smart glasses, other suitableelectronic device, or any suitable combination thereof. In particularembodiments, the client system 130 may be a smart assistant device. Moreinformation on smart assistant devices may be found in U.S. patentapplication Ser. No. 15/949,011, filed 9 Apr. 2018, U.S. patentapplication Ser. No. 16/153,574, filed 5 Oct. 2018, U.S. Design patentapplication No. 29/631910, filed 3 Jan. 2018, U.S. Design patentApplication No. 29/631747, filed 2 Jan. 2018, U.S. Design patentapplication No. 29/631913, filed 3 Jan. 2018, and U.S. Design patentapplication No. 29/631914, filed 3 Jan. 2018, each of which isincorporated by reference. This disclosure contemplates any suitableclient systems 130. A client system 130 may enable a network user at aclient system 130 to access a network 110. A client system 130 mayenable its user to communicate with other users at other client systems130.

In particular embodiments, a client system 130 may include a web browser132, and may have one or more add-ons, plug-ins, or other extensions. Auser at a client system 130 may enter a Uniform Resource Locator (URL)or other address directing a web browser 132 to a particular server(such as server 162, or a server associated with a third-party system170), and the web browser 132 may generate a Hyper Text TransferProtocol (HTTP) request and communicate the HTTP request to server. Theserver may accept the HTTP request and communicate to a client system130 one or more Hyper Text Markup Language (HTML) files responsive tothe HTTP request. The client system 130 may render a web interface (e.g.a webpage) based on the HTML files from the server for presentation tothe user. This disclosure contemplates any suitable source files. As anexample and not by way of limitation, a web interface may be renderedfrom HTML files, Extensible Hyper Text Markup Language (XHTML) files, orExtensible Markup Language (XML) files, according to particular needs.Such interfaces may also execute scripts, combinations of markuplanguage and scripts, and the like. Herein, reference to a web interfaceencompasses one or more corresponding source files (which a browser mayuse to render the web interface) and vice versa, where appropriate.

In particular embodiments, a client system 130 may include asocial-networking application 134 installed on the client system 130. Auser at a client system 130 may use the social-networking application134 to access on online social network. The user at the client system130 may use the social-networking application 134 to communicate withthe user's social connections (e.g., friends, followers, followedaccounts, contacts, etc.). The user at the client system 130 may alsouse the social-networking application 134 to interact with a pluralityof content objects (e.g., posts, news articles, ephemeral content, etc.)on the online social network. As an example and not by way oflimitation, the user may browse trending topics and breaking news usingthe social-networking application 134.

In particular embodiments, a client system 130 may include an assistantapplication 136. A user at a client system 130 may use the assistantapplication 136 to interact with the assistant system 140. In particularembodiments, the assistant application 136 may comprise a stand-aloneapplication. In particular embodiments, the assistant application 136may be integrated into the social-networking application 134 or anothersuitable application (e.g., a messaging application). In particularembodiments, the assistant application 136 may be also integrated intothe client system 130, an assistant hardware device, or any othersuitable hardware devices. In particular embodiments, the assistantapplication 136 may be accessed via the web browser 132. In particularembodiments, the user may provide input via different modalities. As anexample and not by way of limitation, the modalities may include audio,text, image, video, motion, orientation, etc. The assistant application136 may communicate the user input to the assistant system 140. Based onthe user input, the assistant system 140 may generate responses. Theassistant system 140 may send the generated responses to the assistantapplication 136. The assistant application 136 may then present theresponses to the user at the client system 130. The presented responsesmay be based on different modalities such as audio, text, image, andvideo. As an example and not by way of limitation, the user may verballyask the assistant application 136 about the traffic information (i.e.,via an audio modality) by speaking into a microphone of the clientsystem 130. The assistant application 136 may then communicate therequest to the assistant system 140. The assistant system 140 mayaccordingly generate a response and send it back to the assistantapplication 136. The assistant application 136 may further present theresponse to the user in text and/or images on a display of the clientsystem 130.

In particular embodiments, an assistant system 140 may assist users toretrieve information from different sources. The assistant system 140may also assist user to request services from different serviceproviders. In particular embodiments, the assist system 140 may receivea user request for information or services via the assistant application136 in the client system 130. The assist system 140 may usenatural-language understanding to analyze the user request based onuser's profile and other relevant information. The result of theanalysis may comprise different entities associated with an onlinesocial network. The assistant system 140 may then retrieve informationor request services associated with these entities. In particularembodiments, the assistant system 140 may interact with thesocial-networking system 160 and/or third-party system 170 whenretrieving information or requesting services for the user. Inparticular embodiments, the assistant system 140 may generate apersonalized communication content for the user using natural-languagegenerating techniques. The personalized communication content maycomprise, for example, the retrieved information or the status of therequested services. In particular embodiments, the assistant system 140may enable the user to interact with it regarding the information orservices in a stateful and multi-turn conversation by usingdialog-management techniques. The functionality of the assistant system140 is described in more detail in the discussion of FIG. 2 below.

In particular embodiments, the social-networking system 160 may be anetwork-addressable computing system that can host an online socialnetwork. The social-networking system 160 may generate, store, receive,and send social-networking data, such as, for example, user profiledata, concept-profile data, social-graph information, or other suitabledata related to the online social network. The social-networking system160 may be accessed by the other components of network environment 100either directly or via a network 110. As an example and not by way oflimitation, a client system 130 may access the social-networking system160 using a web browser 132, or a native application associated with thesocial-networking system 160 (e.g., a mobile social-networkingapplication, a messaging application, another suitable application, orany combination thereof) either directly or via a network 110. Inparticular embodiments, the social-networking system 160 may include oneor more servers 162. Each server 162 may be a unitary server or adistributed server spanning multiple computers or multiple datacenters.Servers 162 may be of various types, such as, for example and withoutlimitation, web server, news server, mail server, message server,advertising server, file server, application server, exchange server,database server, proxy server, another server suitable for performingfunctions or processes described herein, or any combination thereof. Inparticular embodiments, each server 162 may include hardware, software,or embedded logic components or a combination of two or more suchcomponents for carrying out the appropriate functionalities implementedor supported by server 162. In particular embodiments, thesocial-networking system 160 may include one or more data stores 164.Data stores 164 may be used to store various types of information. Inparticular embodiments, the information stored in data stores 164 may beorganized according to specific data structures. In particularembodiments, each data store 164 may be a relational, columnar,correlation, or other suitable database. Although this disclosuredescribes or illustrates particular types of databases, this disclosurecontemplates any suitable types of databases. Particular embodiments mayprovide interfaces that enable a client system 130, a social-networkingsystem 160, an assistant system 140, or a third-party system 170 tomanage, retrieve, modify, add, or delete, the information stored in datastore 164.

In particular embodiments, the social-networking system 160 may storeone or more social graphs in one or more data stores 164. In particularembodiments, a social graph may include multiple nodes—which may includemultiple user nodes (each corresponding to a particular user) ormultiple concept nodes (each corresponding to a particular concept)—andmultiple edges connecting the nodes. The social-networking system 160may provide users of the online social network the ability tocommunicate and interact with other users. In particular embodiments,users may join the online social network via the social-networkingsystem 160 and then add connections (e.g., relationships) to a number ofother users of the social-networking system 160 whom they want to beconnected to. Herein, the term “friend” may refer to any other user ofthe social-networking system 160 with whom a user has formed aconnection, association, or relationship via the social-networkingsystem 160.

In particular embodiments, the social-networking system 160 may provideusers with the ability to take actions on various types of items orobjects, supported by the social-networking system 160. As an exampleand not by way of limitation, the items and objects may include groupsor social networks to which users of the social-networking system 160may belong, events or calendar entries in which a user might beinterested, computer-based applications that a user may use,transactions that allow users to buy or sell items via the service,interactions with advertisements that a user may perform, or othersuitable items or objects. A user may interact with anything that iscapable of being represented in the social-networking system 160 or byan external system of a third-party system 170, which is separate fromthe social-networking system 160 and coupled to the social-networkingsystem 160 via a network 110.

In particular embodiments, the social-networking system 160 may becapable of linking a variety of entities. As an example and not by wayof limitation, the social-networking system 160 may enable users tointeract with each other as well as receive content from third-partysystems 170 or other entities, or to allow users to interact with theseentities through an application programming interfaces (API) or othercommunication channels.

In particular embodiments, a third-party system 170 may include one ormore types of servers, one or more data stores, one or more interfaces,including but not limited to APIs, one or more web services, one or morecontent sources, one or more networks, or any other suitable components,e.g., that servers may communicate with. A third-party system 170 may beoperated by a different entity from an entity operating thesocial-networking system 160. In particular embodiments, however, thesocial-networking system 160 and third-party systems 170 may operate inconjunction with each other to provide social-networking services tousers of the social-networking system 160 or third-party systems 170. Inthis sense, the social-networking system 160 may provide a platform, orbackbone, which other systems, such as third-party systems 170, may useto provide social-networking services and functionality to users acrossthe Internet.

In particular embodiments, a third-party system 170 may include athird-party content object provider. A third-party content objectprovider may include one or more sources of content objects, which maybe communicated to a client system 130. As an example and not by way oflimitation, content objects may include information regarding things oractivities of interest to the user, such as, for example, movie showtimes, movie reviews, restaurant reviews, restaurant menus, productinformation and reviews, or other suitable information. As anotherexample and not by way of limitation, content objects may includeincentive content objects, such as coupons, discount tickets, giftcertificates, or other suitable incentive objects. In particularembodiments, a third-party content provider may use one or morethird-party agents to provide content objects and/or services. Athird-party agent may be an implementation that is hosted and executingon the third-party system 170.

In particular embodiments, the social-networking system 160 alsoincludes user-generated content objects, which may enhance a user'sinteractions with the social-networking system 160. User-generatedcontent may include anything a user can add, upload, send, or “post” tothe social-networking system 160. As an example and not by way oflimitation, a user communicates posts to the social-networking system160 from a client system 130. Posts may include data such as statusupdates or other textual data, location information, photos, videos,links, music or other similar data or media. Content may also be addedto the social-networking system 160 by a third-party through a“communication channel,” such as a newsfeed or stream.

In particular embodiments, the social-networking system 160 may includea variety of servers, sub-systems, programs, modules, logs, and datastores. In particular embodiments, the social-networking system 160 mayinclude one or more of the following: a web server, action logger,API-request server, relevance-and-ranking engine, content-objectclassifier, notification controller, action log,third-party-content-object-exposure log, inference module,authorization/privacy server, search module, advertisement-targetingmodule, user-interface module, user-profile store, connection store,third-party content store, or location store. The social-networkingsystem 160 may also include suitable components such as networkinterfaces, security mechanisms, load balancers, failover servers,management-and-network-operations consoles, other suitable components,or any suitable combination thereof. In particular embodiments, thesocial-networking system 160 may include one or more user-profile storesfor storing user profiles. A user profile may include, for example,biographic information, demographic information, behavioral information,social information, or other types of descriptive information, such aswork experience, educational history, hobbies or preferences, interests,affinities, or location. Interest information may include interestsrelated to one or more categories. Categories may be general orspecific. As an example and not by way of limitation, if a user “likes”an article about a brand of shoes the category may be the brand, or thegeneral category of “shoes” or “clothing.” A connection store may beused for storing connection information about users. The connectioninformation may indicate users who have similar or common workexperience, group memberships, hobbies, educational history, or are inany way related or share common attributes. The connection informationmay also include user-defined connections between different users andcontent (both internal and external). A web server may be used forlinking the social-networking system 160 to one or more client systems130 or one or more third-party systems 170 via a network 110. The webserver may include a mail server or other messaging functionality forreceiving and routing messages between the social-networking system 160and one or more client systems 130. An API-request server may allow, forexample, an assistant system 140 or a third-party system 170 to accessinformation from the social-networking system 160 by calling one or moreAPIs. An action logger may be used to receive communications from a webserver about a user's actions on or off the social-networking system160. In conjunction with the action log, a third-party-content-objectlog may be maintained of user exposures to third-party-content objects.A notification controller may provide information regarding contentobjects to a client system 130. Information may be pushed to a clientsystem 130 as notifications, or information may be pulled from a clientsystem 130 responsive to a request received from a client system 130.Authorization servers may be used to enforce one or more privacysettings of the users of the social-networking system 160. A privacysetting of a user determines how particular information associated witha user can be shared. The authorization server may allow users to opt into or opt out of having their actions logged by the social-networkingsystem 160 or shared with other systems (e.g., a third-party system170), such as, for example, by setting appropriate privacy settings.Third-party-content-object stores may be used to store content objectsreceived from third parties, such as a third-party system 170. Locationstores may be used for storing location information received from clientsystems 130 associated with users. Advertisement-pricing modules maycombine social information, the current time, location information, orother suitable information to provide relevant advertisements, in theform of notifications, to a user.

Assistant Systems

FIG. 2 illustrates an example architecture 200 of an assistant system140. In particular embodiments, the assistant system 140 may assist auser to obtain information or services. The assistant system 140 mayenable the user to interact with it with multi-modal user input (such asvoice, text, image, video, motion) in stateful and multi-turnconversations to get assistance. As an example and not by way oflimitation, the assistant system 140 may support both audio input(verbal) and nonverbal input, such as vision, location, gesture, motion,or hybrid/multi-modal input. The assistant system 140 may create andstore a user profile comprising both personal and contextual informationassociated with the user. In particular embodiments, the assistantsystem 140 may analyze the user input using natural-languageunderstanding. The analysis may be based on the user profile of the userfor more personalized and context-aware understanding. The assistantsystem 140 may resolve entities associated with the user input based onthe analysis. In particular embodiments, the assistant system 140 mayinteract with different agents to obtain information or services thatare associated with the resolved entities. The assistant system 140 maygenerate a response for the user regarding the information or servicesby using natural-language generation. Through the interaction with theuser, the assistant system 140 may use dialog management techniques tomanage and forward the conversation flow with the user. In particularembodiments, the assistant system 140 may further assist the user toeffectively and efficiently digest the obtained information bysummarizing the information. The assistant system 140 may also assistthe user to be more engaging with an online social network by providingtools that help the user interact with the online social network (e.g.,creating posts, comments, messages). The assistant system 140 mayadditionally assist the user to manage different tasks such as keepingtrack of events. In particular embodiments, the assistant system 140 mayproactively execute, without a user input, pre-authorized tasks that arerelevant to user interests and preferences based on the user profile, ata time relevant for the user. In particular embodiments, the assistantsystem 140 may check privacy settings to ensure that accessing a user'sprofile or other user information and executing different tasks arepermitted subject to the user's privacy settings. More information onassisting users subject to privacy settings may be found in U.S. patentapplication Ser. No. 16/182,542, filed 6 Nov. 2018, which isincorporated by reference.

In particular embodiments, the assistant system 140 may assist the uservia a hybrid architecture built upon both client-side processes andserver-side processes. The client-side processes and the server-sideprocesses may be two parallel workflows for processing a user input andproviding assistances to the user. In particular embodiments, theclient-side processes may be performed locally on a client system 130associated with a user. By contrast, the server-side processes may beperformed remotely on one or more computing systems. In particularembodiments, an assistant orchestrator on the client system 130 maycoordinate receiving user input (e.g., audio signal) and determinewhether to use client-side processes, server-side processes, or both, torespond to the user input. A dialog arbitrator may analyze theprocessing results from each process. The dialog arbitrator may instructagents on the client-side or server-side to execute tasks associatedwith the user input based on the aforementioned analyses. The executionresults may be further rendered as output to the client system 130. Byleveraging both client-side and server-side processes, the assistantsystem 140 can effectively assist a user with optimal usage of computingresources while at the same time protecting user privacy and enhancingsecurity.

In particular embodiments, the assistant system 140 may receive a userinput from a client system 130 associated with the user. In particularembodiments, the user input may be a user-generated input that is sentto the assistant system 140 in a single turn. The user input may beverbal, nonverbal, or a combination thereof. As an example and not byway of limitation, the nonverbal user input may be based on the user'svoice, vision, location, activity, gesture, motion, or a combinationthereof. If the user input is based on the user's voice (e.g., the usermay speak to the client system 130), such user input may be firstprocessed by a system audio API 202 (application programming interface).The system audio API 202 may conduct echo cancellation, noise removal,beam forming, and self-user voice activation, speaker identification,voice activity detection (VAD), and any other acoustic techniques togenerate audio data that is readily processable by the assistant system140. In particular embodiments, the system audio API 202 may performwake-word detection 204 from the user input. As an example and not byway of limitation, a wake-word may be “hey assistant”. If such wake-wordis detected, the assistant system 140 may be activated accordingly. Inalternative embodiments, the user may activate the assistant system 140via a visual signal without a wake-word. The visual signal may bereceived at a low-power sensor (e.g., a camera) that can detect variousvisual signals. As an example and not by way of limitation, the visualsignal may be a barcode, a QR code or a universal product code (UPC)detected by the client system 130. As another example and not by way oflimitation, the visual signal may be the user's gaze at an object. Asyet another example and not by way of limitation, the visual signal maybe a user gesture, e.g., the user pointing at an object.

In particular embodiments, the audio data from the system audio API 202may be sent to an assistant orchestrator 206. The assistant orchestrator206 may be executing on the client system 130. In particularembodiments, the assistant orchestrator 206 may determine whether torespond to the user input by using client-side processes, server-sideprocesses, or both. As indicated in FIG. 2, the client-side processesare illustrated below the dashed line 207 whereas the server-sideprocesses are illustrated above the dashed line 207. The assistantorchestrator 206 may also determine to respond to the user input byusing both the client-side processes and the server-side processessimultaneously. Although FIG. 2 illustrates the assistant orchestrator206 as being a client-side process, the assistant orchestrator 206 maybe a server-side process or may be a hybrid process split betweenclient- and server-side processes.

In particular embodiments, the server-side processes may be as followsafter audio data is generated from the system audio API 202. Theassistant orchestrator 206 may send the audio data to a remote computingsystem that hosts different modules of the assistant system 140 torespond to the user input. In particular embodiments, the audio data maybe received at a remote automatic speech recognition (ASR) module 208 a.The ASR module 208 a may allow a user to dictate and have speechtranscribed as written text, have a document synthesized as an audiostream, or issue commands that are recognized as such by the system. TheASR module 208 a may use statistical models to determine the most likelysequences of words that correspond to a given portion of speech receivedby the assistant system 140 as audio input. The models may include oneor more of hidden Markov models, neural networks, deep learning models,or any combination thereof. The received audio input may be encoded intodigital data at a particular sampling rate (e.g., 16, 44.1, or 96 kHz)and with a particular number of bits representing each sample (e.g., 8,16, of 24 bits).

In particular embodiments, the ASR module 208 a may comprise differentcomponents. The ASR module 208 a may comprise one or more of agrapheme-to-phoneme (G2P) model, a pronunciation learning model, apersonalized acoustic model, a personalized language model (PLM), or anend-pointing model. In particular embodiments, the G2P model may be usedto determine a user's grapheme-to-phoneme style, e.g., what it may soundlike when a particular user speaks a particular word. The personalizedacoustic model may be a model of the relationship between audio signalsand the sounds of phonetic units in the language. Therefore, suchpersonalized acoustic model may identify how a user's voice sounds. Thepersonalized acoustical model may be generated using training data suchas training speech received as audio input and the correspondingphonetic units that correspond to the speech. The personalizedacoustical model may be trained or refined using the voice of aparticular user to recognize that user's speech. In particularembodiments, the personalized language model may then determine the mostlikely phrase that corresponds to the identified phonetic units for aparticular audio input. The personalized language model may be a modelof the probabilities that various word sequences may occur in thelanguage. The sounds of the phonetic units in the audio input may bematched with word sequences using the personalized language model, andgreater weights may be assigned to the word sequences that are morelikely to be phrases in the language. The word sequence having thehighest weight may be then selected as the text that corresponds to theaudio input. In particular embodiments, the personalized language modelmay be also used to predict what words a user is most likely to saygiven a context. In particular embodiments, the end-pointing model maydetect when the end of an utterance is reached.

In particular embodiments, the output of the ASR module 208 a may besent to a remote natural-language understanding (NLU) module 210 a. TheNLU module 210 a may perform named entity resolution (NER). The NLUmodule 210 a may additionally consider contextual information whenanalyzing the user input. In particular embodiments, an intent and/or aslot may be an output of the NLU module 210 a. An intent may be anelement in a pre-defined taxonomy of semantic intentions, which mayindicate a purpose of a user interacting with the assistant system 140.The NLU module 210 a may classify a user input into a member of thepre-defined taxonomy, e.g., for the input “Play Beethoven's 5th,” theNLU module 210 a may classify the input as having the intent[IN:play_music]. In particular embodiments, a domain may denote a socialcontext of interaction, e.g., education, or a namespace for a set ofintents, e.g., music. A slot may be a named sub-string corresponding toa character string within the user input, representing a basic semanticentity. For example, a slot for “pizza” may be [SL:dish]. In particularembodiments, a set of valid or expected named slots may be conditionedon the classified intent. As an example and not by way of limitation,for the intent [IN:play_music], a valid slot may be [SL:song_name]. Inparticular embodiments, the NLU module 210 a may additionally extractinformation from one or more of a social graph, a knowledge graph, or aconcept graph, and retrieve a user's profile from one or more remotedata stores 212. The NLU module 210 a may further process informationfrom these different sources by determining what information toaggregate, annotating n-grams of the user input, ranking the n-gramswith confidence scores based on the aggregated information, andformulating the ranked n-grams into features that can be used by the NLUmodule 210 a for understanding the user input.

In particular embodiments, the NLU module 210 a may identify one or moreof a domain, an intent, or a slot from the user input in a personalizedand context-aware manner. As an example and not by way of limitation, auser input may comprise “show me how to get to the coffee shop”. The NLUmodule 210 a may identify the particular coffee shop that the user wantsto go based on the user's personal information and the associatedcontextual information. In particular embodiments, the NLU module 210 amay comprise a lexicon of a particular language and a parser and grammarrules to partition sentences into an internal representation. The NLUmodule 210 a may also comprise one or more programs that perform naivesemantics or stochastic semantic analysis to the use of pragmatics tounderstand a user input. In particular embodiments, the parser may bebased on a deep learning architecture comprising multiple long-shortterm memory (LSTM) networks. As an example and not by way of limitation,the parser may be based on a recurrent neural network grammar (RNNG)model, which is a type of recurrent and recursive LSTM algorithm. Moreinformation on natural-language understanding may be found in U.S.patent application Ser. No. 16/011,062, filed 18 Jun. 2018, U.S. patentapplication Ser. No. 16/025,317, filed 2 Jul. 2018, and U.S. patentapplication Ser. No. 16/038,120, filed 17 Jul. 2018, each of which isincorporated by reference.

In particular embodiments, the output of the NLU module 210 a may besent to a remote reasoning module 212 a. The reasoning module 212 a maycomprise a dialog manager and an entity resolution component. Inparticular embodiments, the dialog manager may have complex dialog logicand product-related business logic. The dialog manager may manage thedialog state and flow of the conversation between the user and theassistant system 140. The dialog manager may additionally store previousconversations between the user and the assistant system 140. Inparticular embodiments, the dialog manager may communicate with theentity resolution component to resolve entities associated with the oneor more slots, which supports the dialog manager to advance the flow ofthe conversation between the user and the assistant system 140. Inparticular embodiments, the entity resolution component may access oneor more of the social graph, the knowledge graph, or the concept graphwhen resolving the entities. Entities may include, for example, uniqueusers or concepts, each of which may have a unique identifier (ID). Asan example and not by way of limitation, the knowledge graph maycomprise a plurality of entities. Each entity may comprise a singlerecord associated with one or more attribute values. The particularrecord may be associated with a unique entity identifier. Each recordmay have diverse values for an attribute of the entity. Each attributevalue may be associated with a confidence probability. A confidenceprobability for an attribute value represents a probability that thevalue is accurate for the given attribute. Each attribute value may bealso associated with a semantic weight. A semantic weight for anattribute value may represent how the value semantically appropriate forthe given attribute considering all the available information. Forexample, the knowledge graph may comprise an entity of a book“BookName”, which includes information that has been extracted frommultiple content sources (e.g., an online social network, onlineencyclopedias, book review sources, media databases, and entertainmentcontent sources), and then deduped, resolved, and fused to generate thesingle unique record for the knowledge graph. The entity may beassociated with a “fantasy” attribute value which indicates the genre ofthe book “BookName”. More information on the knowledge graph may befound in U.S. patent application Ser. No. 16/048,049, filed 27 Jul.2018, and U.S. patent application Ser. No. 16/048,101, filed 27 Jul.2018, each of which is incorporated by reference.

In particular embodiments, the entity resolution component may check theprivacy constraints to guarantee that the resolving of the entities doesnot violate privacy policies. As an example and not by way oflimitation, an entity to be resolved may be another user who specifiesin his/her privacy settings that his/her identity should not besearchable on the online social network, and thus the entity resolutioncomponent may not return that user's identifier in response to arequest. Based on the information obtained from the social graph, theknowledge graph, the concept graph, and the user profile, and subject toapplicable privacy policies, the entity resolution component maytherefore resolve the entities associated with the user input in apersonalized, context-aware, and privacy-aware manner. In particularembodiments, each of the resolved entities may be associated with one ormore identifiers hosted by the social-networking system 160. As anexample and not by way of limitation, an identifier may comprise aunique user identifier (ID) corresponding to a particular user (e.g., aunique username or user ID number). In particular embodiments, each ofthe resolved entities may be also associated with a confidence score.More information on resolving entities may be found in U.S. patentapplication Ser. No. 16/048,049, filed 27 Jul. 2018, and U.S. patentapplication Ser. No. 16/048,072, filed 27 Jul. 2018, each of which isincorporated by reference.

In particular embodiments, the dialog manager may conduct dialogoptimization and assistant state tracking. Dialog optimization is theproblem of using data to understand what the most likely branching in adialog should be. As an example and not by way of limitation, withdialog optimization the assistant system 140 may not need to confirm whoa user wants to call because the assistant system 140 has highconfidence that a person inferred based on dialog optimization would bevery likely whom the user wants to call. In particular embodiments, thedialog manager may use reinforcement learning for dialog optimization.Assistant state tracking aims to keep track of a state that changes overtime as a user interacts with the world and the assistant system 140interacts with the user. As an example and not by way of limitation,assistant state tracking may track what a user is talking about, whomthe user is with, where the user is, what tasks are currently inprogress, and where the user's gaze is at, etc., subject to applicableprivacy policies. In particular embodiments, the dialog manager may usea set of operators to track the dialog state. The operators may comprisethe necessary data and logic to update the dialog state. Each operatormay act as delta of the dialog state after processing an incomingrequest. In particular embodiments, the dialog manager may furthercomprise a dialog state tracker and an action selector. In alternativeembodiments, the dialog state tracker may replace the entity resolutioncomponent and resolve the references/mentions and keep track of thestate.

In particular embodiments, the reasoning module 212 a may furtherconduct false trigger mitigation. The goal of false trigger mitigationis to detect false triggers (e.g., wake-word) of assistance requests andto avoid generating false records when a user actually does not intendto invoke the assistant system 140. As an example and not by way oflimitation, the reasoning module 212 a may achieve false triggermitigation based on a nonsense detector. If the nonsense detectordetermines that a wake-word makes no sense at this point in theinteraction with the user, the reasoning module 212 a may determine thatinferring the user intended to invoke the assistant system 140 may beincorrect. In particular embodiments, the output of the reasoning module212 a may be sent a remote dialog arbitrator 214.

In particular embodiments, each of the ASR module 208 a, NLU module 210a, and reasoning module 212 a may access the remote data store 216,which comprises user episodic memories to determine how to assist a usermore effectively. More information on episodic memories may be found inU.S. patent application Ser. No. 16/552,559, filed 27 Aug. 2019, whichis incorporated by reference. The data store 216 may additionally storethe user profile of the user. The user profile of the user may compriseuser profile data including demographic information, social information,and contextual information associated with the user. The user profiledata may also include user interests and preferences on a plurality oftopics, aggregated through conversations on news feed, search logs,messaging platforms, etc. The usage of a user profile may be subject toprivacy constraints to ensure that a user's information can be used onlyfor his/her benefit, and not shared with anyone else. More informationon user profiles may be found in U.S. patent application Ser. No.15/967,239, filed 30 Apr. 2018, which is incorporated by reference.

In particular embodiments, parallel to the aforementioned server-sideprocess involving the ASR module 208 a, NLU module 210 a, and reasoningmodule 212 a, the client-side process may be as follows. In particularembodiments, the output of the assistant orchestrator 206 may be sent toa local ASR module 208 b on the client system 130. The ASR module 208 bmay comprise a personalized language model (PLM), a G2P model, and anend-pointing model. Because of the limited computing power of the clientsystem 130, the assistant system 140 may optimize the personalizedlanguage model at run time during the client-side process. As an exampleand not by way of limitation, the assistant system 140 may pre-compute aplurality of personalized language models for a plurality of possiblesubjects a user may talk about. When a user requests assistance, theassistant system 140 may then swap these pre-computed language modelsquickly so that the personalized language model may be optimized locallyby the assistant system 140 at run time based on user activities. As aresult, the assistant system 140 may have a technical advantage ofsaving computational resources while efficiently determining what theuser may be talking about. In particular embodiments, the assistantsystem 140 may also re-learn user pronunciations quickly at run time.

In particular embodiments, the output of the ASR module 208 b may besent to a local NLU module 210 b. In particular embodiments, the NLUmodule 210 b herein may be more compact compared to the remote NLUmodule 210 a supported on the server-side. When the ASR module 208 b andNLU module 210 b process the user input, they may access a localassistant memory 218. The local assistant memory 218 may be differentfrom the user memories stored on the data store 216 for the purpose ofprotecting user privacy. In particular embodiments, the local assistantmemory 218 may be syncing with the user memories stored on the datastore 216 via the network 110. As an example and not by way oflimitation, the local assistant memory 218 may sync a calendar on auser's client system 130 with a server-side calendar associate with theuser. In particular embodiments, any secured data in the local assistantmemory 218 may be only accessible to the modules of the assistant system140 that are locally executing on the client system 130.

In particular embodiments, the output of the NLU module 210 b may besent to a local reasoning module 212 b. The reasoning module 212 b maycomprise a dialog manager and an entity resolution component. Due to thelimited computing power, the reasoning module 212 b may conducton-device learning that is based on learning algorithms particularlytailored for client systems 130. As an example and not by way oflimitation, federated learning may be used by the reasoning module 212b. Federated learning is a specific category of distributed machinelearning approaches which trains machine learning models usingdecentralized data residing on end devices such as mobile phones. Inparticular embodiments, the reasoning module 212 b may use a particularfederated learning model, namely federated user representation learning,to extend existing neural-network personalization techniques tofederated learning. Federated user representation learning canpersonalize models in federated learning by learning task-specific userrepresentations (i.e., embeddings) or by personalizing model weights.Federated user representation learning is a simple, scalable,privacy-preserving, and resource-efficient. Federated userrepresentation learning may divide model parameters into federated andprivate parameters. Private parameters, such as private user embeddings,may be trained locally on a client system 130 instead of beingtransferred to or averaged on a remote server. Federated parameters, bycontrast, may be trained remotely on the server. In particularembodiments, the reasoning module 212 b may use another particularfederated learning model, namely active federated learning to transmit aglobal model trained on the remote server to client systems 130 andcalculate gradients locally on these client systems 130. Activefederated learning may enable the reasoning module to minimize thetransmission costs associated with downloading models and uploadinggradients. For active federated learning, in each round client systemsare selected not uniformly at random, but with a probability conditionedon the current model and the data on the client systems to maximizeefficiency. In particular embodiments, the reasoning module 212 b mayuse another particular federated learning model, namely federated Adam.Conventional federated learning model may use stochastic gradientdescent (SGD) optimizers. By contrast, the federated Adam model may usemoment-based optimizers. Instead of using the averaged model directly aswhat conventional work does, federated Adam model may use the averagedmodel to compute approximate gradients. These gradients may be then fedinto the federated Adam model, which may de-noise stochastic gradientsand use a per-parameter adaptive learning rate. Gradients produced byfederated learning may be even noisier than stochastic gradient descent(because data may be not independent and identically distributed), sofederated Adam model may help even more deal with the noise. Thefederated Adam model may use the gradients to take smarter steps towardsminimizing the objective function. The experiments show thatconventional federated learning on a benchmark has 1.6% drop in ROC(Receiver Operating Characteristics) curve whereas federated Adam modelhas only 0.4% drop. In addition, federated Adam model has no increase incommunication or on-device computation. In particular embodiments, thereasoning module 212 b may also perform false trigger mitigation. Thisfalse trigger mitigation may help detect false activation requests,e.g., wake-word, on the client system 130 when the user's speech inputcomprises data that is subject to privacy constraints. As an example andnot by way of limitation, when a user is in a voice call, the user'sconversation is private and the false trigger detection based on suchconversation can only occur locally on the user's client system 130.

In particular embodiments, the assistant system 140 may comprise a localcontext engine 220. The context engine 220 may process all the otheravailable signals to provide more informative cues to the reasoningmodule 212 b. As an example and not by way of limitation, the contextengine 220 may have information related to people, sensory data fromclient system 130 sensors (e.g., microphone, camera) that are furtheranalyzed by computer vision technologies, geometry constructions,activity data, inertial data (e.g., collected by a VR headset),location, etc. In particular embodiments, the computer visiontechnologies may comprise human skeleton reconstruction, face detection,facial recognition, hand tracking, eye tracking, etc. In particularembodiments, geometry constructions may comprise constructing objectssurrounding a user using data collected by a client system 130. As anexample and not by way of limitation, the user may be wearing AR glassesand geometry construction may aim to determine where the floor is, wherethe wall is, where the user's hands are, etc. In particular embodiments,inertial data may be data associated with linear and angular motions. Asan example and not by way of limitation, inertial data may be capturedby AR glasses which measures how a user's body parts move.

In particular embodiments, the output of the local reasoning module 212b may be sent to the dialog arbitrator 214. The dialog arbitrator 214may function differently in three scenarios. In the first scenario, theassistant orchestrator 206 determines to use server-side process, forwhich the dialog arbitrator 214 may transmit the output of the reasoningmodule 212 a to a remote action execution module 222 a. In the secondscenario, the assistant orchestrator 206 determines to use bothserver-side processes and client-side processes, for which the dialogarbitrator 214 may aggregate output from both reasoning modules (i.e.,remote reasoning module 212 a and local reasoning module 212 b) of bothprocesses and analyze them. As an example and not by way of limitation,the dialog arbitrator 214 may perform ranking and select the bestreasoning result for responding to the user input. In particularembodiments, the dialog arbitrator 214 may further determine whether touse agents on the server-side or on the client-side to execute relevanttasks based on the analysis. In the third scenario, the assistantorchestrator 206 determines to use client-side processes and the dialogarbitrator 214 needs to evaluate the output of the local reasoningmodule 212 b to determine if the client-side processes can complete thetask of handling the user input.

In particular embodiments, for the first and second scenarios mentionedabove, the dialog arbitrator 214 may determine that the agents on theserver-side are necessary to execute tasks responsive to the user input.Accordingly, the dialog arbitrator 214 may send necessary informationregarding the user input to the action execution module 222 a. Theaction execution module 222 a may call one or more agents to execute thetasks. In alternative embodiments, the action selector of the dialogmanager may determine actions to execute and instruct the actionexecution module 222 a accordingly. In particular embodiments, an agentmay be an implementation that serves as a broker across a plurality ofcontent providers for one domain. A content provider may be an entityresponsible for carrying out an action associated with an intent orcompleting a task associated with the intent. In particular embodiments,the agents may comprise first-party agents and third-party agents. Inparticular embodiments, first-party agents may comprise internal agentsthat are accessible and controllable by the assistant system 140 (e.g.agents associated with services provided by the online social network,such as messaging services or photo-share services). In particularembodiments, third-party agents may comprise external agents that theassistant system 140 has no control over (e.g., third-party online musicapplication agents, ticket sales agents). The first-party agents may beassociated with first-party providers that provide content objectsand/or services hosted by the social-networking system 160. Thethird-party agents may be associated with third-party providers thatprovide content objects and/or services hosted by the third-party system170. In particular embodiments, each of the first-party agents orthird-party agents may be designated for a particular domain. As anexample and not by way of limitation, the domain may comprise weather,transportation, music, etc. In particular embodiments, the assistantsystem 140 may use a plurality of agents collaboratively to respond to auser input. As an example and not by way of limitation, the user inputmay comprise “direct me to my next meeting.” The assistant system 140may use a calendar agent to retrieve the location of the next meeting.The assistant system 140 may then use a navigation agent to direct theuser to the next meeting.

In particular embodiments, for the second and third scenarios mentionedabove, the dialog arbitrator 214 may determine that the agents on theclient-side are capable of executing tasks responsive to the user inputbut additional information is needed (e.g., response templates) or thatthe tasks can be only handled by the agents on the server-side. If thedialog arbitrator 214 determines that the tasks can be only handled bythe agents on the server-side, the dialog arbitrator 214 may sendnecessary information regarding the user input to the action executionmodule 222 a. If the dialog arbitrator 214 determines that the agents onthe client-side are capable of executing tasks but response templatesare needed, the dialog arbitrator 214 may send necessary informationregarding the user input to a remote response template generation module224. The output of the response template generation module 224 may befurther sent to a local action execution module 222 b executing on theclient system 130.

In particular embodiments, the action execution module 222 b may calllocal agents to execute tasks. A local agent on the client system 130may be able to execute simpler tasks compared to an agent on theserver-side. As an example and not by way of limitation, multipledevice-specific implementations (e.g., real-time calls for a clientsystem 130 or a messaging application on the client system 130) may behandled internally by a single agent. Alternatively, thesedevice-specific implementations may be handled by multiple agentsassociated with multiple domains. In particular embodiments, the actionexecution module 222 b may additionally perform a set of generalexecutable dialog actions. The set of executable dialog actions mayinteract with agents, users and the assistant system 140 itself. Thesedialog actions may comprise dialog actions for slot request,confirmation, disambiguation, agent execution, etc. The dialog actionsmay be independent of the underlying implementation of the actionselector or dialog policy. Both tree-based policy and model-based policymay generate the same basic dialog actions, with a callback functionhiding any action selector specific implementation details.

In particular embodiments, the output from the remote action executionmodule 222 a on the server-side may be sent to a remote responseexecution module 226 a. In particular embodiments, the action executionmodule 222 a may communicate back to the dialog arbitrator 214 for moreinformation. The response execution module 226 a may be based on aremote conversational understanding (CU) composer. In particularembodiments, the output from the action execution module 222 a may beformulated as a <k, c, u, d> tuple, in which k indicates a knowledgesource, c indicates a communicative goal, u indicates a user model, andd indicates a discourse model. In particular embodiments, the CUcomposer may comprise a natural-language generation (NLG) module and auser interface (UI) payload generator. The natural-language generatormay generate a communication content based on the output of the actionexecution module 222 a using different language models and/or languagetemplates. In particular embodiments, the generation of thecommunication content may be application specific and also personalizedfor each user. The CU composer may also determine a modality of thegenerated communication content using the UI payload generator. Inparticular embodiments, the NLG module may comprise a contentdetermination component, a sentence planner, and a surface realizationcomponent. The content determination component may determine thecommunication content based on the knowledge source, communicative goal,and the user's expectations. As an example and not by way of limitation,the determining may be based on a description logic. The descriptionlogic may comprise, for example, three fundamental notions which areindividuals (representing objects in the domain), concepts (describingsets of individuals), and roles (representing binary relations betweenindividuals or concepts). The description logic may be characterized bya set of constructors that allow the natural-language generator to buildcomplex concepts/roles from atomic ones. In particular embodiments, thecontent determination component may perform the following tasks todetermine the communication content. The first task may comprise atranslation task, in which the input to the natural-language generatormay be translated to concepts. The second task may comprise a selectiontask, in which relevant concepts may be selected among those resultedfrom the translation task based on the user model. The third task maycomprise a verification task, in which the coherence of the selectedconcepts may be verified. The fourth task may comprise an instantiationtask, in which the verified concepts may be instantiated as anexecutable file that can be processed by the natural-language generator.The sentence planner may determine the organization of the communicationcontent to make it human understandable. The surface realizationcomponent may determine specific words to use, the sequence of thesentences, and the style of the communication content. The UI payloadgenerator may determine a preferred modality of the communicationcontent to be presented to the user. In particular embodiments, the CUcomposer may check privacy constraints associated with the user to makesure the generation of the communication content follows the privacypolicies. More information on natural-language generation may be foundin U.S. patent application Ser. No. 15/967,279, filed 30 Apr. 2018, andU.S. patent application Ser. No. 15/966,455, filed 30 Apr. 2018, each ofwhich is incorporated by reference.

In particular embodiments, the output from the local action executionmodule 222 b on the client system 130 may be sent to a local responseexecution module 226 b. The response execution module 226 b may be basedon a local conversational understanding (CU) composer. The CU composermay comprise a natural-language generation (NLG) module. As thecomputing power of a client system 130 may be limited, the NLG modulemay be simple for the consideration of computational efficiency. Becausethe NLG module may be simple, the output of the response executionmodule 226 b may be sent to a local response expansion module 228. Theresponse expansion module 228 may further expand the result of theresponse execution module 226 b to make a response more natural andcontain richer semantic information.

In particular embodiments, if the user input is based on audio signals,the output of the response execution module 226 a on the server-side maybe sent to a remote text-to-speech (TTS) module 230 a. Similarly, theoutput of the response expansion module 228 on the client-side may besent to a local TTS module 230 b. Both TTS modules may convert aresponse to audio signals. In particular embodiments, the output fromthe response execution module 226 a, the response expansion module 228,or the TTS modules on both sides, may be finally sent to a local renderoutput module 232. The render output module 232 may generate a responsethat is suitable for the client system 130. As an example and not by wayof limitation, the output of the response execution module 226 a or theresponse expansion module 228 may comprise one or more ofnatural-language strings, speech, actions with parameters, or renderedimages or videos that can be displayed in a VR headset or AR smartglasses. As a result, the render output module 232 may determine whattasks to perform based on the output of CU composer to render theresponse appropriately for displaying on the VR headset or AR smartglasses. For example, the response may be visual-based modality (e.g.,an image or a video clip) that can be displayed via the VR headset or ARsmart glasses. As another example, the response may be audio signalsthat can be played by the user via VR headset or AR smart glasses. Asyet another example, the response may be augmented-reality data that canbe rendered VR headset or AR smart glasses for enhancing userexperience.

In particular embodiments, the assistant system 140 may have a varietyof capabilities including audio cognition, visual cognition, signalsintelligence, reasoning, and memories. In particular embodiments, thecapability of audio recognition may enable the assistant system 140 tounderstand a user's input associated with various domains in differentlanguages, understand a conversation and be able to summarize it,perform on-device audio cognition for complex commands, identify a userby voice, extract topics from a conversation and auto-tag sections ofthe conversation, enable audio interaction without a wake-word, filterand amplify user voice from ambient noise and conversations, understandwhich client system 130 (if multiple client systems 130 are in vicinity)a user is talking to.

In particular embodiments, the capability of visual cognition may enablethe assistant system 140 to perform face detection and tracking,recognize a user, recognize most people of interest in majormetropolitan areas at varying angles, recognize majority of interestingobjects in the world through a combination of existing machine-learningmodels and one-shot learning, recognize an interesting moment andauto-capture it, achieve semantic understanding over multiple visualframes across different episodes of time, provide platform support foradditional capabilities in people, places, objects recognition,recognize full set of settings and micro-locations includingpersonalized locations, recognize complex activities, recognize complexgestures to control a client system 130, handle images/videos fromegocentric cameras (e.g., with motion, capture angles, resolution,etc.), accomplish similar level of accuracy and speed regarding imageswith lower resolution, conduct one-shot registration and recognition ofpeople, places, and objects, and perform visual recognition on a clientsystem 130.

In particular embodiments, the assistant system 140 may leveragecomputer vision techniques to achieve visual cognition. Besides computervision techniques, the assistant system 140 may explore options that cansupplement these techniques to scale up the recognition of objects. Inparticular embodiments, the assistant system 140 may use supplementalsignals such as optical character recognition (OCR) of an object'slabels, GPS signals for places recognition, signals from a user's clientsystem 130 to identify the user. In particular embodiments, theassistant system 140 may perform general scene recognition (home, work,public space, etc.) to set context for the user and reduce thecomputer-vision search space to identify top likely objects or people.In particular embodiments, the assistant system 140 may guide users totrain the assistant system 140. For example, crowdsourcing may be usedto get users to tag and help the assistant system 140 recognize moreobjects over time. As another example, users can register their personalobjects as part of initial setup when using the assistant system 140.The assistant system 140 may further allow users to providepositive/negative signals for objects they interact with to train andimprove personalized models for them.

In particular embodiments, the capability of signals intelligence mayenable the assistant system 140 to determine user location, understanddate/time, determine family locations, understand users' calendars andfuture desired locations, integrate richer sound understanding toidentify setting/context through sound alone, build signals intelligencemodels at run time which may be personalized to a user's individualroutines.

In particular embodiments, the capability of reasoning may enable theassistant system 140 to have the ability to pick up any previousconversation threads at any point in the future, synthesize all signalsto understand micro and personalized context, learn interaction patternsand preferences from users' historical behavior and accurately suggestinteractions that they may value, generate highly predictive proactivesuggestions based on micro-context understanding, understand whatcontent a user may want to see at what time of a day, understand thechanges in a scene and how that may impact the user's desired content.

In particular embodiments, the capabilities of memories may enable theassistant system 140 to remember which social connections a userpreviously called or interacted with, write into memory and query memoryat will (i.e., open dictation and auto tags), extract richer preferencesbased on prior interactions and long-term learning, remember a user'slife history, extract rich information from egocentric streams of dataand auto catalog, and write to memory in structured form to form richshort, episodic and long-term memories.

FIG. 3 illustrates an example flow diagram 300 of server-side processesof the assistant system 140. In particular embodiments, aserver-assistant service module 301 may access a request manager 302upon receiving a user request. In alternative embodiments, the userrequest may be first processed by the remote ASR module 208 a if theuser request is based on audio signals. In particular embodiments, therequest manager 302 may comprise a context extractor 303 and aconversational understanding object generator (CU object generator) 304.The context extractor 303 may extract contextual information associatedwith the user request. The context extractor 303 may also updatecontextual information based on the assistant application 136 executingon the client system 130. As an example and not by way of limitation,the update of contextual information may comprise content items aredisplayed on the client system 130. As another example and not by way oflimitation, the update of contextual information may comprise whether analarm is set on the client system 130. As another example and not by wayof limitation, the update of contextual information may comprise whethera song is playing on the client system 130. The CU object generator 304may generate particular content objects relevant to the user request.The content objects may comprise dialog-session data and featuresassociated with the user request, which may be shared with all themodules of the assistant system 140. In particular embodiments, therequest manager 302 may store the contextual information and thegenerated content objects in data store 216 which is a particular datastore implemented in the assistant system 140.

In particular embodiments, the request manger 302 may send the generatedcontent objects to the remote NLU module 210 a. The NLU module 210 a mayperform a plurality of steps to process the content objects. At step305, the NLU module 210 a may generate a whitelist for the contentobjects. In particular embodiments, the whitelist may compriseinterpretation data matching the user request. At step 306, the NLUmodule 210 a may perform a featurization based on the whitelist. At step307, the NLU module 210 a may perform domain classification/selection onuser request based on the features resulted from the featurization toclassify the user request into predefined domains. The domainclassification/selection results may be further processed based on tworelated procedures. At step 308 a, the NLU module 210 a may process thedomain classification/selection result using an intent classifier. Theintent classifier may determine the user's intent associated with theuser request. In particular embodiments, there may be one intentclassifier for each domain to determine the most possible intents in agiven domain. As an example and not by way of limitation, the intentclassifier may be based on a machine-learning model that may take thedomain classification/selection result as input and calculate aprobability of the input being associated with a particular predefinedintent. At step 308 b, the NLU module 210 a may process the domainclassification/selection result using a meta-intent classifier. Themeta-intent classifier may determine categories that describe the user'sintent. In particular embodiments, intents that are common to multipledomains may be processed by the meta-intent classifier. As an exampleand not by way of limitation, the meta-intent classifier may be based ona machine-learning model that may take the domainclassification/selection result as input and calculate a probability ofthe input being associated with a particular predefined meta-intent. Atstep 309 a, the NLU module 210 a may use a slot tagger to annotate oneor more slots associated with the user request. In particularembodiments, the slot tagger may annotate the one or more slots for then-grams of the user request. At step 309 b, the NLU module 210 a may usea meta slot tagger to annotate one or more slots for the classificationresult from the meta-intent classifier. In particular embodiments, themeta slot tagger may tag generic slots such as references to items(e.g., the first), the type of slot, the value of the slot, etc. As anexample and not by way of limitation, a user request may comprise“change 500 dollars in my account to Japanese yen.” The intentclassifier may take the user request as input and formulate it into avector. The intent classifier may then calculate probabilities of theuser request being associated with different predefined intents based ona vector comparison between the vector representing the user request andthe vectors representing different predefined intents. In a similarmanner, the slot tagger may take the user request as input and formulateeach word into a vector. The intent classifier may then calculateprobabilities of each word being associated with different predefinedslots based on a vector comparison between the vector representing theword and the vectors representing different predefined slots. The intentof the user may be classified as “changing money”. The slots of the userrequest may comprise “500”, “dollars”, “account”, and “Japanese yen”.The meta-intent of the user may be classified as “financial service”.The meta slot may comprise “finance”.

In particular embodiments, the NLU module 210 a may comprise a semanticinformation aggregator 310. The semantic information aggregator 310 mayhelp the NLU module 210 a improve the domain classification/selection ofthe content objects by providing semantic information. In particularembodiments, the semantic information aggregator 310 may aggregatesemantic information in the following way. The semantic informationaggregator 310 may first retrieve information from a user context engine315. In particular embodiments, the user context engine 315 may compriseoffline aggregators and an online inference service. The offlineaggregators may process a plurality of data associated with the userthat are collected from a prior time window. As an example and not byway of limitation, the data may include news feed posts/comments,interactions with news feed posts/comments, search history, etc., thatare collected during a predetermined timeframe (e.g., from a prior90-day window). The processing result may be stored in the user contextengine 315 as part of the user profile. The online inference service mayanalyze the conversational data associated with the user that arereceived by the assistant system 140 at a current time. The analysisresult may be stored in the user context engine 315 also as part of theuser profile. In particular embodiments, both the offline aggregatorsand online inference service may extract personalization features fromthe plurality of data. The extracted personalization features may beused by other modules of the assistant system 140 to better understanduser input. In particular embodiments, the semantic informationaggregator 310 may then process the retrieved information, i.e., a userprofile, from the user context engine 315 in the following steps. Atstep 311, the semantic information aggregator 310 may process theretrieved information from the user context engine 315 based onnatural-language processing (NLP). In particular embodiments, thesemantic information aggregator 310 may tokenize text by textnormalization, extract syntax features from text, and extract semanticfeatures from text based on NLP. The semantic information aggregator 310may additionally extract features from contextual information, which isaccessed from dialog history between a user and the assistant system140. The semantic information aggregator 310 may further conduct globalword embedding, domain-specific embedding, and/or dynamic embeddingbased on the contextual information. At step 312, the processing resultmay be annotated with entities by an entity tagger. Based on theannotations, the semantic information aggregator 310 may generatedictionaries for the retrieved information at step 313. In particularembodiments, the dictionaries may comprise global dictionary featureswhich can be updated dynamically offline. At step 314, the semanticinformation aggregator 310 may rank the entities tagged by the entitytagger. In particular embodiments, the semantic information aggregator310 may communicate with different graphs 320 including one or more ofthe social graph, the knowledge graph, or the concept graph to extractontology data that is relevant to the retrieved information from theuser context engine 315. In particular embodiments, the semanticinformation aggregator 310 may aggregate the user profile, the rankedentities, and the information from the graphs 320. The semanticinformation aggregator 310 may then provide the aggregated informationto the NLU module 210 a to facilitate the domainclassification/selection.

In particular embodiments, the output of the NLU module 210 a may besent to the remote reasoning module 212 a. The reasoning module 212 amay comprise a co-reference component 325, an entity resolutioncomponent 330, and a dialog manager 335. The output of the NLU module210 a may be first received at the co-reference component 325 tointerpret references of the content objects associated with the userrequest. In particular embodiments, the co-reference component 325 maybe used to identify an item to which the user request refers. Theco-reference component 325 may comprise reference creation 326 andreference resolution 327. In particular embodiments, the referencecreation 326 may create references for entities determined by the NLUmodule 210 a. The reference resolution 327 may resolve these referencesaccurately. As an example and not by way of limitation, a user requestmay comprise “find me the nearest grocery store and direct me there”.The co-reference component 325 may interpret “there” as “the nearestgrocery store”. In particular embodiments, the co-reference component325 may access the user context engine 315 and the dialog manager 335when necessary to interpret references with improved accuracy.

In particular embodiments, the identified domains, intents,meta-intents, slots, and meta slots, along with the resolved referencesmay be sent to the entity resolution component 330 to resolve relevantentities. The entity resolution component 330 may execute generic anddomain-specific entity resolution. In particular embodiments, the entityresolution component 330 may comprise domain entity resolution 331 andgeneric entity resolution 332. The domain entity resolution 331 mayresolve the entities by categorizing the slots and meta slots intodifferent domains. In particular embodiments, entities may be resolvedbased on the ontology data extracted from the graphs 320. The ontologydata may comprise the structural relationship between differentslots/meta-slots and domains. The ontology may also comprise informationof how the slots/meta-slots may be grouped, related within a hierarchywhere the higher level comprises the domain, and subdivided according tosimilarities and differences. The generic entity resolution 332 mayresolve the entities by categorizing the slots and meta slots intodifferent generic topics. In particular embodiments, the resolving maybe also based on the ontology data extracted from the graphs 320. Theontology data may comprise the structural relationship between differentslots/meta-slots and generic topics. The ontology may also compriseinformation of how the slots/meta-slots may be grouped, related within ahierarchy where the higher level comprises the topic, and subdividedaccording to similarities and differences. As an example and not by wayof limitation, in response to the input of an inquiry of the advantagesof a particular brand of electric car, the generic entity resolution 332may resolve the referenced brand of electric car as vehicle and thedomain entity resolution 331 may resolve the referenced brand ofelectric car as electric car.

In particular embodiments, the output of the entity resolution component330 may be sent to the dialog manager 335 to advance the flow of theconversation with the user. The dialog manager 335 may be anasynchronous state machine that repeatedly updates the state and selectsactions based on the new state. The dialog manager 335 may comprisedialog intent resolution 336 and dialog state tracker 337. In particularembodiments, the dialog manager 335 may execute the selected actions andthen call the dialog state tracker 337 again until the action selectedrequires a user response, or there are no more actions to execute. Eachaction selected may depend on the execution result from previousactions. In particular embodiments, the dialog intent resolution 336 mayresolve the user intent associated with the current dialog session basedon dialog history between the user and the assistant system 140. Thedialog intent resolution 336 may map intents determined by the NLUmodule 210 a to different dialog intents. The dialog intent resolution336 may further rank dialog intents based on signals from the NLU module210 a, the entity resolution component 330, and dialog history betweenthe user and the assistant system 140. In particular embodiments,instead of directly altering the dialog state, the dialog state tracker337 may be a side-effect free component and generate n-best candidatesof dialog state update operators that propose updates to the dialogstate. The dialog state tracker 337 may comprise intent resolverscontaining logic to handle different types of NLU intent based on thedialog state and generate the operators. In particular embodiments, thelogic may be organized by intent handler, such as a disambiguationintent handler to handle the intents when the assistant system 140 asksfor disambiguation, a confirmation intent handler that comprises thelogic to handle confirmations, etc. Intent resolvers may combine theturn intent together with the dialog state to generate the contextualupdates for a conversation with the user. A slot resolution componentmay then recursively resolve the slots in the update operators withresolution providers including the knowledge graph and domain agents. Inparticular embodiments, the dialog state tracker 337 may update/rank thedialog state of the current dialog session. As an example and not by wayof limitation, the dialog state tracker 337 may update the dialog stateas “completed” if the dialog session is over. As another example and notby way of limitation, the dialog state tracker 337 may rank the dialogstate based on a priority associated with it.

In particular embodiments, the reasoning module 212 a may communicatewith the remote action execution module 222 a and the dialog arbitrator214, respectively. In particular embodiments, the dialog manager 335 ofthe reasoning module 212 a may communicate with a task completioncomponent 340 of the action execution module 222 a about the dialogintent and associated content objects. In particular embodiments, thetask completion module 340 may rank different dialog hypotheses fordifferent dialog intents. The task completion module 340 may comprise anaction selector 341. In alternative embodiments, the action selector 341may be comprised in the dialog manager 335. In particular embodiments,the dialog manager 335 may additionally check against dialog policies345 comprised in the dialog arbitrator 214 regarding the dialog state.In particular embodiments, a dialog policy 345 may comprise a datastructure that describes an execution plan of an action by an agent 350.The dialog policy 345 may comprise a general policy 346 and taskpolicies 347. In particular embodiments, the general policy 346 may beused for actions that are not specific to individual tasks. The generalpolicy 346 may comprise handling low confidence intents, internalerrors, unacceptable user response with retries, skipping or insertingconfirmation based on ASR or NLU confidence scores, etc. The generalpolicy 346 may also comprise the logic of ranking dialog state updatecandidates from the dialog state tracker 337 output and pick the one toupdate (such as picking the top ranked task intent). In particularembodiments, the assistant system 140 may have a particular interfacefor the general policy 346, which allows for consolidating scatteredcross-domain policy/business-rules, especial those found in the dialogstate tracker 337, into a function of the action selector 341. Theinterface for the general policy 346 may also allow for authoring ofself-contained sub-policy units that may be tied to specific situationsor clients, e.g., policy functions that may be easily switched on or offbased on clients, situation, etc. The interface for the general policy346 may also allow for providing a layering of policies with back-off,i.e. multiple policy units, with highly specialized policy units thatdeal with specific situations being backed up by more general policies346 that apply in wider circumstances. In this context the generalpolicy 346 may alternatively comprise intent or task specific policy. Inparticular embodiments, a task policy 347 may comprise the logic foraction selector 341 based on the task and current state. In particularembodiments, the types of task policies 347 may include one or more ofthe following types: (1) manually crafted tree-based dialog plans; (2)coded policy that directly implements the interface for generatingactions; (3) configurator-specified slot-filling tasks; or (4)machine-learning model based policy learned from data. In particularembodiments, the assistant system 140 may bootstrap new domains withrule-based logic and later refine the task policies 347 withmachine-learning models. In particular embodiments, a dialog policy 345may a tree-based policy, which is a pre-constructed dialog plan. Basedon the current dialog state, a dialog policy 345 may choose a node toexecute and generate the corresponding actions. As an example and not byway of limitation, the tree-based policy may comprise topic groupingnodes and dialog action (leaf) nodes.

In particular embodiments, the action selector 341 may take candidateoperators of dialog state and consult the dialog policy 345 to decidewhat action should be executed. The assistant system 140 may use ahierarchical dialog policy with general policy 346 handling thecross-domain business logic and task policies 347 handles thetask/domain specific logic. In particular embodiments, the generalpolicy 346 may pick one operator from the candidate operators to updatethe dialog state, followed by the selection of a user facing action by atask policy 347. Once a task is active in the dialog state, thecorresponding task policy 347 may be consulted to select right actions.In particular embodiments, both the dialog state tracker 337 and theaction selector 341 may not change the dialog state until the selectedaction is executed. This may allow the assistant system 140 to executethe dialog state tracker 337 and the action selector 341 for processingspeculative ASR results and to do n-best ranking with dry runs. Inparticular embodiments, the action selector 341 may take the dialogstate update operators as part of the input to select the dialog action.The execution of the dialog action may generate a set of expectation toinstruct the dialog state tracker 337 to handler future turns. Inparticular embodiments, an expectation may be used to provide context tothe dialog state tracker 337 when handling the user input from nextturn. As an example and not by way of limitation, slot request dialogaction may have the expectation of proving a value for the requestedslot.

In particular embodiments, the dialog manager 335 may support multi-turncompositional resolution of slot mentions. For a compositional parsefrom the NLU 210 a, the resolver may recursively resolve the nestedslots. The dialog manager 335 may additionally support disambiguationfor the nested slots. As an example and not by way of limitation, theuser request may be “remind me to call Alex”. The resolver may need toknow which Alex to call before creating an actionable reminder to-doentity. The resolver may halt the resolution and set the resolutionstate when further user clarification is necessary for a particularslot. The general policy 346 may examine the resolution state and createcorresponding dialog action for user clarification. In dialog statetracker 337, based on the user request and the last dialog action, thedialog manager may update the nested slot. This capability may allow theassistant system 140 to interact with the user not only to collectmissing slot values but also to reduce ambiguity of morecomplex/ambiguous utterances to complete the task. In particularembodiments, the dialog manager may further support requesting missingslots in a nested intent and multi-intent user requests (e.g., “takethis photo and send it to Dad”). In particular embodiments, the dialogmanager 335 may support machine-learning models for more robust dialogexperience. As an example and not by way of limitation, the dialog statetracker 337 may use neural network based models (or any other suitablemachine-learning models) to model belief over task hypotheses. Asanother example and not by way of limitation, for action selector 341,highest priority policy units may comprise white-list/black-listoverrides, which may have to occur by design; middle priority units maycomprise machine-learning models designed for action selection; andlower priority units may comprise rule-based fallbacks when themachine-learning models elect not to handle a situation. In particularembodiments, machine-learning model based general policy unit may helpthe assistant system 140 reduce redundant disambiguation or confirmationsteps, thereby reducing the number of turns to execute the user request.

In particular embodiments, the action execution module 222 a may calldifferent agents 350 for task execution. An agent 350 may select amongregistered content providers to complete the action. The data structuremay be constructed by the dialog manager 335 based on an intent and oneor more slots associated with the intent. A dialog policy 345 mayfurther comprise multiple goals related to each other through logicaloperators. In particular embodiments, a goal may be an outcome of aportion of the dialog policy and it may be constructed by the dialogmanager 335. A goal may be represented by an identifier (e.g., string)with one or more named arguments, which parameterize the goal. As anexample and not by way of limitation, a goal with its associated goalargument may be represented as {confirm_artist, args: {artist:“Madonna”}}. In particular embodiments, a dialog policy may be based ona tree-structured representation, in which goals are mapped to leaves ofthe tree. In particular embodiments, the dialog manager 335 may executea dialog policy 345 to determine the next action to carry out. Thedialog policies 345 may comprise generic policy 346 and domain specificpolicies 347, both of which may guide how to select the next systemaction based on the dialog state. In particular embodiments, the taskcompletion component 340 of the action execution module 222 a maycommunicate with dialog policies 345 comprised in the dialog arbitrator214 to obtain the guidance of the next system action. In particularembodiments, the action selection component 341 may therefore select anaction based on the dialog intent, the associated content objects, andthe guidance from dialog policies 345.

In particular embodiments, the output of the action execution module 222a may be sent to the remote response execution module 226 a.Specifically, the output of the task completion component 340 of theaction execution module 222 a may be sent to the CU composer 355 of theresponse execution module 226 a. In alternative embodiments, theselected action may require one or more agents 350 to be involved. As aresult, the task completion module 340 may inform the agents 350 aboutthe selected action. Meanwhile, the dialog manager 335 may receive aninstruction to update the dialog state. As an example and not by way oflimitation, the update may comprise awaiting agents' 350 response. Inparticular embodiments, the CU composer 355 may generate a communicationcontent for the user using a natural-language generation (NLG) module356 based on the output of the task completion module 340. In particularembodiments, the NLG module 356 may use different language models and/orlanguage templates to generate natural language outputs. The generationof natural language outputs may be application specific. The generationof natural language outputs may be also personalized for each user. TheCU composer 355 may also determine a modality of the generatedcommunication content using the UI payload generator 357. Since thegenerated communication content may be considered as a response to theuser request, the CU composer 355 may additionally rank the generatedcommunication content using a response ranker 358. As an example and notby way of limitation, the ranking may indicate the priority of theresponse.

In particular embodiments, the response execution module 226 a mayperform different tasks based on the output of the CU composer 355.These tasks may include writing (i.e., storing/updating) the dialogstate 361 retrieved from data store 216 and generating responses 362. Inparticular embodiments, the output of CU composer 355 may comprise oneor more of natural-language strings, speech, actions with parameters, orrendered images or videos that can be displayed in a VR headset or ARsmart glass. As a result, the response execution module 226 a maydetermine what tasks to perform based on the output of CU composer 355.In particular embodiments, the generated response and the communicationcontent may be sent to the local render output module 232 by theresponse execution module 226 a. In alternative embodiments, the outputof the CU composer 355 may be additionally sent to the remote TTS module230 a if the determined modality of the communication content is audio.The speech generated by the TTS module 230 a and the response generatedby the response execution module 226 a may be then sent to the renderoutput module 232.

FIG. 4 illustrates an example flow diagram 400 of processing a userinput by the assistant system 140. As an example and not by way oflimitation, the user input may be based on audio signals. In particularembodiments, a mic array 402 of the client system 130 may receive theaudio signals (e.g., speech). The audio signals may be transmitted to aprocess loop 404 in a format of audio frames. In particular embodiments,the process loop 404 may send the audio frames for voice activitydetection (VAD) 406 and wake-on-voice (WoV) detection 408. The detectionresults may be returned to the process loop 404. If the WoV detection408 indicates the user wants to invoke the assistant system 140, theaudio frames together with the VAD 406 result may be sent to an encodeunit 410 to generate encoded audio data. After encoding, the encodedaudio data may be sent to an encrypt unit 412 for privacy and securitypurpose, followed by a link unit 414 and decrypt unit 416. Afterdecryption, the audio data may be sent to a mic driver 418, which mayfurther transmit the audio data to an audio service module 420. Inalternative embodiments, the user input may be received at a wirelessdevice (e.g., Bluetooth device) paired with the client system 130.Correspondingly, the audio data may be sent from a wireless-devicedriver 422 (e.g., Bluetooth driver) to the audio service module 420. Inparticular embodiments, the audio service module 420 may determine thatthe user input can be fulfilled by an application executing on theclient system 130. Accordingly, the audio service module 420 may sendthe user input to a real-time communication (RTC) module 424. The RTCmodule 424 may deliver audio packets to a video or audio communicationsystem (e.g., VOIP or video call). The RTC module 424 may call arelevant application (App) 426 to execute tasks related to the userinput.

In particular embodiments, the audio service module 420 may determinethat the user is requesting assistance that needs the assistant system140 to respond. Accordingly, the audio service module 420 may inform theclient-assistant service module 428. In particular embodiments, theclient-assistant service module 428 may communicate with the assistantorchestrator 206. The assistant orchestrator 206 may determine whetherto use client-side processes or server-side processes to respond to theuser input. In particular embodiments, the assistant orchestrator 206may determine to use client-side processes and inform theclient-assistant service module 428 about such decision. As a result,the client-assistant service module 428 may call relevant modules torespond to the user input.

In particular embodiments, the client-assistant service module 428 mayuse the local ASR module 208 b to analyze the user input. The ASR module208 b may comprise a grapheme-to-phoneme (G2P) model, a pronunciationlearning model, a personalized language model (PLM), an end-pointingmodel, and a personalized acoustic model. In particular embodiments, theclient-assistant service module 428 may further use the local NLU module210 b to understand the user input. The NLU module 210 b may comprise anamed entity resolution (NER) component and a contextual session-basedNLU component. In particular embodiments, the client-assistant servicemodule 428 may use an intent broker 430 to analyze the user's intent. Tobe accurate about the user's intent, the intent broker 430 may access anentity store 432 comprising entities associated with the user and theworld. In alternative embodiments, the user input may be submitted viaan application 434 executing on the client system 130. In this case, aninput manager 436 may receive the user input and analyze it by anapplication environment (App Env) module 438. The analysis result may besent to the application 434 which may further send the analysis resultto the ASR module 208 b and NLU module 210 b. In alternativeembodiments, the user input may be directly submitted to theclient-assistant service module 428 via an assistant application 440executing on the client system 130. Then the client-assistant servicemodule 428 may perform similar procedures based on modules asaforementioned, i.e., the ASR module 208 b, the NLU module 210 b, andthe intent broker 430.

In particular embodiments, the assistant orchestrator 206 may determineto user server-side process. Accordingly, the assistant orchestrator 206may send the user input to one or more computing systems that hostdifferent modules of the assistant system 140. In particularembodiments, a server-assistant service module 301 may receive the userinput from the assistant orchestrator 206. The server-assistant servicemodule 301 may instruct the remote ASR module 208 a to analyze the audiodata of the user input. The ASR module 208 a may comprise agrapheme-to-phoneme (G2P) model, a pronunciation learning model, apersonalized language model (PLM), an end-pointing model, and apersonalized acoustic model. In particular embodiments, theserver-assistant service module 301 may further instruct the remote NLUmodule 210 a to understand the user input. In particular embodiments,the server-assistant service module 301 may call the remote reasoningmodel 212 a to process the output from the ASR module 208 a and the NLUmodule 210 a. In particular embodiments, the reasoning model 212 a mayperform entity resolution and dialog optimization. In particularembodiments, the output of the reasoning model 212 a may be sent to theagent 350 for executing one or more relevant tasks.

In particular embodiments, the agent 350 may access an ontology module442 to accurately understand the result from entity resolution anddialog optimization so that it can execute relevant tasks accurately.The ontology module 442 may provide ontology data associated with aplurality of predefined domains, intents, and slots. The ontology datamay also comprise the structural relationship between different slotsand domains. The ontology data may further comprise information of howthe slots may be grouped, related within a hierarchy where the higherlevel comprises the domain, and subdivided according to similarities anddifferences. The ontology data may also comprise information of how theslots may be grouped, related within a hierarchy where the higher levelcomprises the topic, and subdivided according to similarities anddifferences. Once the tasks are executed, the agent 350 may return theexecution results together with a task completion indication to thereasoning module 212 a.

The embodiments disclosed herein may include or be implemented inconjunction with an artificial reality system. Artificial reality is aform of reality that has been adjusted in some manner beforepresentation to a user, which may include, e.g., a virtual reality (VR),an augmented reality (AR), a mixed reality (MR), a hybrid reality, orsome combination and/or derivatives thereof. Artificial reality contentmay include completely generated content or generated content combinedwith captured content (e.g., real-world photographs). The artificialreality content may include video, audio, haptic feedback, or somecombination thereof, and any of which may be presented in a singlechannel or in multiple channels (such as stereo video that produces athree-dimensional effect to the viewer). Additionally, in someembodiments, artificial reality may be associated with applications,products, accessories, services, or some combination thereof, that are,e.g., used to create content in an artificial reality and/or used in(e.g., perform activities in) an artificial reality. The artificialreality system that provides the artificial reality content may beimplemented on various platforms, including a head-mounted display (HMD)connected to a host computer system, a standalone HMD, a mobile deviceor computing system, or any other hardware platform capable of providingartificial reality content to one or more viewers.

In-Call Experience Enhancement

In particular embodiment, an in-call experience enhancement in which theassistant system is persistently active, but on standby during a call(such as a video or audio call) or other communication session (such asa text message thread), is provided. Such a persistently activeassistant system may enable a user to invoke it in real-time during thecall to execute tasks related to one or more other users on the call.Furthermore, the persistently active assistant system may allow a singlecommunication domain to be used in which the user can communicate withboth other people via the call and with the assistant system itself.Current assistant systems typically go dormant during calls, so that auser must pause the call and reawaken the assistant system in order toissue commands. Thus, this single communication domain may greatlyimprove the user's experience, enabling a more social and naturalinteraction. The persistent assistant system may utilize an underlyingmultimodal architecture having separate context and scene understandingengines. The context engine may also be persistent during the call,gathering data for use by other modules in the assistant system thatresponds to a user query (subject to privacy settings). By contrast, thescene understanding engine may be awakened as needed to receive the datagathered by the context engine and determines a relationship amongdetected entities. Accordingly, with a video call in particular servingas a social experience backdrop, this persistent assistant system mayenable numerous social, utility, communication, and image processingfunctionalities to be performed.

Although this disclosure describes providing a persistent assistantsystem and particular social functions in a particular manner, thisdisclosure contemplates providing a persistent assistant system and anysuitable social functions in any suitable manner.

FIG. 5 illustrates an example multimodal architecture of the assistantsystem 140. The persistent assistant system 140 may use such anunderlying multimodal architecture with separate context and sceneunderstanding engines, as well as various sensors within the user devicehosting the assistant system 140. Sensor/gaze information 501 andpassive audio 502 (e.g., background audio picked up by a microphone onthe client system 130) may be gathered (subject to privacy settings),along with a content vector 504 derived from the image 503 (e.g., inputfrom a camera), and input into the context engine 510. The contextengine 510 may generate a context 511. This context 511 and/or anygesture information 512 may then be input into a non-verbal intentrecognizer 513 to determine an intent of the user input. Detection of awake-word 514 may trigger capture of active audio 515 (e.g., a user'saudio input to the assistant system 140), which may then be input intoan ASR module 208 to generate text 517. This text 517, and/or anyinitial text 518 input by a user, may be transmitted to the NLU module210 to generate semantic information 519. Sensor/gaze information 501,passive audio 502, image information 503, active audio 515, and/or text517 may be input into the scene understanding engine 520, which may inturn exchange data with the NLU 210. Output of the scene understandingengine 520 may then be input into the non-verbal intent recognizer 513and/or into a dialog module 530. Data from an assistant user memory 550may also be input into the non-verbal intent recognizer 513 and/or intothe dialog module 530. An assistant state tracker module 531, whichincludes a context tracker 532, a resolver 533, and a task state tracker534, receives these various data items as input. Output from theassistant state tracker 531 then may be input into an action selector341, which in turn may include a general policy unit 346 and a taskpolicy unit 347. The output of the dialog module 530 may be sent to ASR208 to invoke heavy processing, for example, when a request thatrequires an understanding of the scene is received. As an example andnot by way of limitation, a user viewing an image or video stream mayrequest “tell me more about the dog” to the assistant system 140.Segmenting out objects from the image may be resource intensive andunnecessary to answer most requests, so the heavy processing ofsegmenting objects (for example, to segment out the dog referred to inthe request) may only be performed when requested, rather thancontinuously.

FIG. 6A illustrates an example initial scene 600 viewed during a videocall on a first client system 130 of a first user. In particularembodiments, the assistant system 140 may establish a video call betweena plurality of client systems 130. Each client system 130 may beassociated with one or more users (e.g., participants in the videocall). The assistant system 140 may receive a request from the firstclient system 130 of the first user identifying one or more other usersto add to a video call and may assign a call identifier (ID) to thevideo call. As an example and not by way of limitation, the assistantsystem 140 may use this call ID in monitoring the video call and contextinformation of the scene 600 and of various client systems 130participating in it. In particular embodiments, the assistant system 140may itself be added as a participant in the video call, subject toprivacy settings of each of the users of the video call. As an exampleand not by way of limitation, a first user may request to add theassistant system 140 to the video call, and, if each of the other userspermit, the assistant system 140 may be added as a participant to thevideo call; otherwise, the assistant system 140 may not be added. Inparticular embodiments, the assistant system 140 may establish a videocall customized for business. As an example and not by way oflimitation, the first user may communicate with customer service agentsvia the video call (for example, to show a defective product theyreceived, or to get help with setting up a new product); this assistantsystem 140 may conceal identifying information of the first user duringthis video call for privacy. Although this disclosure describesestablishing a video call in a particular manner, this disclosurecontemplates establishing a video call in any suitable manner.

In particular embodiments, the assistant system 140 may receive, fromthe first client system 130 from among the plurality of client systems130, a request from the first user of the first client system 130 to beperformed by the persistent assistant system 140 during the video call.The request may be a manual request, a spoken request, a gesture as arequest, other suitable input associated with a request, or anycombination thereof. The request may reference one or more second usersassociated with the plurality of client systems 130 in the video call.As an example and not by way of limitation, the first user Alice may beon a video call with the second users Bob, Sarah, and Carol. Alice maythen speak to the assistant system 140 and say “Hey Assistant, take aphoto of Bob,” referencing the second user Bob on the video call.Although this disclosure describes receiving user requests in aparticular manner, this disclosure contemplates receiving user requestsin any suitable manner.

In particular embodiments, the assistant system 140 may receive awake-word 514 that precedes the request and, in response to receivingthis wake-word 514, may send instructions to the first client system 130to mute the video call at the first client system 130. As an example andnot by way of limitation, the first user may say “Hey Assistant,” duringa video call and, upon detecting the wake-word “Hey Assistant”indicating that the first user is about to make a request of theassistant system 140, the assistant system 140 may maintain user privacyby muting the video call on the first client system 130 so that otherusers participating in the video call cannot hear the first user'srequest. However, in particular embodiments, the first user mayexplicitly instruct the first client system 130 to mute the video call(e.g., through a spoken request like “Hey Assistant, mute me,” or theselection of a button or icon for muting the call) while communicatingwith the assistant system 140. Although this disclosure describesdetecting a wake-word in a particular manner, this disclosurecontemplates detecting a wake-word in any suitable manner.

In particular embodiments, the assistant system 140 may detect a gaze ofthe first user directed at one or more entities in the video call andmay infer the request based on the gaze. Such gaze detection may be madeusing, for example, eye tracking. As an example and not by way oflimitation, the assistant system may detect that the first userrepeatedly looks at a clock and infer that he wants to know if a task onhis calendar occurs soon. The assistant system 140 may accordinglyinform him, without any manual or spoken input on the part of the firstuser, that the next task on his calendar (such as a meeting) starts infive minutes, and that he should head toward the meeting location whileon the video call. As another example and not by way of limitation, theassistant system 140 may detect that the first user repeatedly looks ata second user, and may infer that the first user wants to know theidentity of the second user, or wants the smart camera to focus on thatsecond user. The assistant system 140 may thus accordingly inform thefirst user as to the identity of the second user, and/or track thesecond user with the smart camera. Although this disclosure describesinferring a request based on gaze estimation in a particular manner,this disclosure contemplates inferring a request in any suitable manner.

In particular embodiments, the request received by the assistant system140 may reference one or more second users associated with the pluralityof client systems 130 in the video call. The request may explicitlyrefer to a second user by name, or it may imply a second user through arelationship with another second user (e.g., “who is the person to theleft of Alice?”). As an example and not by way of limitation, therequest may be an instruction to focus the display of the first clientsystem 130 on one or more of the second users, as discussed below inwith respect to FIG. 7A. As another example and not by way oflimitation, the request may be an instruction to repeat or summarizespeech of one or more of the second users. As another example and not byway of limitation, the request may comprise an instruction to perform avirtual activity with respect to one or more of the second users, asdiscussed below with respect to FIG. 8. As yet another example and notby way of limitation, the request may be an instruction to share acontent item with one or more of the second users, as discussed belowwith respect to FIG. 9. Although this disclosure describes receivingrequests referencing users in a particular manner, this disclosurecontemplates receiving requests referencing users in any suitablemanner.

Certain technical challenges exist in maintaining a quality video callbetween users. Video calls may lack a feeling of genuine socialinteraction; providing more social functions that may be performedduring the actual video call may thus increase user interaction andsatisfaction with the video call. However, one technical challenge tothis may include identifying users in the video call that the first userin the video call wants to perform some social function with, as well asactually understanding the scene and context of the video call in orderto more accurately execute the social function. A solution presented byembodiments disclosed herein to address this challenge may thus includecontinuously gathering context of the video call via the context engine510 and feeding this gathered information into the scene understandingengine 520, in order to generate relationship information between peopleand objects in the scene of the video call. Further, certain embodimentsdisclosed herein may provide one or more technical advantages. As anexample, accurately identifying users and objects in the video call, aswell as their context and relationship information (subject to privacysettings), may enable the first user to perform a variety of socialfunctions with respect to entities in the video call, even when thefirst user communicates those functions ambiguously.

In particular embodiments, upon receiving the request from the firstclient system 130 referencing one or more second users, the assistantsystem 140 may determine an intent of the request and one or more useridentifiers (user IDs) of the one or more second users referenced by therequest. Accurately identifying users and objects in a video call, aswell as their context and relationship information, may enable the firstuser to perform a variety of social functions with respect to entitiesin the video call, even when the first user communicates those functionsambiguously. As an example and not by way of limitation, the assistantsystem 140 may receive and execute a request to focus a camera on aparticular person. In particular embodiments, the assistant system 140may determine the one or more user identifiers of the one or more secondusers referenced by the request through determination of the secondusers' respective user IDs or through facial recognition of the secondusers (subject to privacy settings). Upon recognizing a given user, theassistant system 140 may assign them a user ID. As an example and not byway of limitation, both active users currently using client systems 130and background users viewable in the frame of the video call may beidentified. In particular embodiments, the identified users may bemodified dynamically as, for example, people enter and leave the frameof the video call, as discussed below with respect to FIGS. 6B and 7B.Although this disclosure describes determining intent and user IDs in aparticular manner, this disclosure contemplates determining intent anduser IDs in any suitable manner.

In particular embodiments, the assistant system 140 may determine thatthe intent of the request is to modify a characteristic (e.g., anappearance or voice) of one or more second users. In particularembodiments, this intent may be determined as an explicit command tomodify this characteristic. As an example and not by way of limitation,the assistant system 140 may thus modify the appearance of the seconduser having the identified user identifier by adding a mask or specialeffects to the second user on the display of the first client system130, as discussed below with respect to FIG. 8. Although this disclosuredescribes determining an intent in a particular manner, this disclosurecontemplates determining an intent in any suitable manner.

In particular embodiments, the assistant system 140 may instruct theassistant system to execute the request based on the determined intentand user IDs. This request may be executed on either or both of theclient-side process or the server-side process of a hybrid assistantsystem. As an example and not by way of limitation, if the intent of therequest indicates that additional user information is needed to executethe request, the assistant system 140 may, subject to privacy settings,retrieve user profile information of one or more of the identifiedsecond users in response to this determined intent and the one or moreuser identifiers and generate the response based on this retrieved userprofile information. As an example and not by way of limitation, theuser profile information may indicate information of an interest orrecent activity of one or more of the second users. To execute therequest, the assistant system 140 may make use of the context engine andscene understanding engine of the underlying multimodal architecture.Although this disclosure describes executing requests in a particularmanner, this disclosure contemplates executing requests in any suitablemanner.

In particular embodiments, the assistant system 140 may access, from thecontext engine 510 of the assistant system 140, context data associatedwith the video call. This context data may indicate properties of ascene of the video call. As an example and not by way of limitation, thecontext data may indicate identifications of objects within the scene.As another example and not by way of limitation, the context data mayindicate user IDs of users within the scene. In particular embodiments,the context engine 510 may analyze these properties of the scene in realtime during the actual video call. The context engine 510 may analyzethese properties through facial, activity, or object recognition, andenter the detected context data into chart 650. As an example and not byway of limitation, the assistant system 140 may determine the identitiesof particular people (Alice and Bob), their activities (standing andspeaking), and their location. Although this disclosure describesaccessing context information in a particular manner, this disclosurecontemplates accessing context information in any suitable manner.

In particular embodiments, the context engine 510 may always be onduring the video call, gathering intelligence for use later in thepipeline of multimodal architecture 500 that responds to a user query.The context engine 510 may thus function as a sort of ambient mode ofthe assistant system 140, constantly monitoring the video call as wellas the first user and capturing information that may be needed torespond to a future user request. Using sensors such as a smart camera,which may also be always on, the context engine 510 may identifyparticular objects (e.g., a water bottle in user Alice's hand),activities (e.g., whether a user is cooking or standing), or locations(e.g., whether the user is at a museum or a concert) in scene 600 viewedduring the video call. The context engine 510 may further continuouslytrack the first user of client device 130 himself, such as through eyetracking/gaze estimation. Although this disclosure describes operating acontext engine in a particular manner, this disclosure contemplatesoperating a context engine in any suitable manner.

FIG. 6B illustrates an example chart 650 of information of the scene 600generated by the always-on context engine 510. This chart 650 may be theoutput of context engine 510 when analyzing scene 600. The chart 650 mayinclude various categories, such as social presence 651, user activityclass 652, focal object recognition 653, user location 654, orsignificant events detection 655. The social presence category 651 mayinclude social information of people in the scene 600 of the video call,allowing particular individuals to be recognized. As somefunctionalities of the assistant system 140 may require it to be ableidentify particular people within the scene 600 (for example, informingthe first user of the name of an unknown second user in the scene ormodifying the appearance of a particular second user), user recognitionmay enable these user-specific functions to be performed. User activityclass 652 may indicate current activity of a detected user, classifiedinto a taxonomy of activity classes; and user location 654 may indicatedeeper knowledge information about the location of a user on a personal,group, or world-knowledge basis. Focal object recognition 653 mayindicate segmented, classified objects from a computer vision system orspatially indexed object database, together with gaze or gesture inputto identify which object a viewing user is focusing on, or which is mostsalient to this user. Significant events detection 655 may encompasswhat is happening around a user in the scene 600; public and privateevents may be detected or inferred based on the current activity,location, or context of a user. In particular embodiments, the contextengine 510 may detect context changes, and trigger a series of events inresponse to relevant changes in downstream components, which may beregistered to particular events in order to effect particular actions.As examples and not by way of limitation, such context changes may bepeople entering or exiting the scene, detection of a new object,determining that a person or object has been recorded for a thresholdamount of time, movements to or from another movement type, starting orending a particular activity, a user arriving or leaving a location, anddetecting when a user has been in a current location for longer than athreshold amount of time.

With reference to FIG. 6A, when monitoring scene 600, context engine 510may detect people 602-605, and determine the respective identity of each(e.g., Bob 602, Alice 603, Carol 604, and Sarah 605). These detectedpeople may be entered into the “Social Presence” category 651 of chart650. Context engine 510 may further detect various activities performedby users in the scene 600 (e.g., walking, standing, eating, orspeaking); these detected activities may be entered into the “UserActivity Class” 652 of chart 650. Context engine 510 may further detectobjects 610-617 (e.g., cat 610, coat 611, water bottle 612, table 613,hat 614, glasses 615, and candy bowl 616) and enter detected objectsinto the “Focal Object Recognition” category 653 of chart 650. Alocation of the monitored scene 600, such as an address, building, orroom of the scene, may be determined and entered into the “UserLocation” category of chart 650. A type of event, such as partyoccurring at the determined location, may further be detected, andentered into the “Significant Events Detection” category of chart 650.

This information gathered by the context engine 510 may enable theassistant system 140 to execute various user- or object-specificfunctions. As an example and not by way of limitation, a user may make arequest such as “where is Bob?”, and a camera of a client device at theviewed scene may locate and focus on Bob, as the context engine 510 hasalready identified which person is Bob. Similarly, a query of “who isspeaking?” may result in this camera focusing on a speaker (e.g., Alice)while displaying the speaker's name, and a request to take a picture ofthe speaker may activate the camera to take the requested photo. Asanother example and not by way of limitation, a request such as “followthe cat” may trigger the camera to locate and track the cat, as thecontext engine 510 has already identified various objects, including thecat, in scene 600.

In particular embodiments, to answer more complex user questions andrequests dealing with inter-object relationships, the assistant system140 may access, from a scene-understanding engine of the assistantsystem 140, relationship data associated with the video call. Thisrelationship data may indicate relationships between various entitieswithin the scene of the video call. In particular embodiments, theassistant system 140 may determine that the request references aparticular type of relationship data. As an example and not by way oflimitation, the request may include a relationship word such as“holding” or “left of”, each of which indicates a different type ofrelationship among entities in the scene. In response to determiningthat the request references this particular type of relationship data,the assistant system 140 may active scene understanding engine 520. Inparticular embodiments, upon being activated, the scene understandingengine 520 may analyze the video call to generate relationship data ofthe particular type of relationship data referenced in the request. Thescene understanding engine 520 may generate this relationship data inreal time in response to being activated, and, after the relationshipdata has been generated, the assistant system 140 may deactivate thescene understanding engine 520.

FIG. 6C illustrates an example knowledge graph 660 of the scene 600generated by scene understanding engine 520. While context engine 510may always be on, the scene understanding engine 520 may be awakened asneeded. The scene understanding engine 520 may receive data (such as thedata of chart 650) tracked by the context engine 510, and determinerelationships among the various detected entities, including both peopleand objects. As an example and not by way of limitation, the sceneunderstanding engine 520 may determine that Sarah is holding the candybowl (that the relationship between entities “Sarah” and “candy bowl”that have been identified by the context engine 510 is “holding”), thatSarah is looking at the cat, that the cat is carrying chicken, thatAlice is to the left of Bob, etc. The scene understanding engine 520 maygenerate knowledge graph 660 of entity relationships; this knowledgegraph 660 may be generated on and concern only the scene 600 of thevideo call. Because determining such semantic information may becomputationally expensive, the scene understanding engine 520 may beawakened in response to a request that includes a relationship word(such as “holding” or “left of”), rather than remaining always on (or inambient mode) like the context engine 510. However, even in embodimentsin which the scene understanding engine 520 awakens only upon request,the scene understanding engine 520 may be able to generate theinformation needed for a response relatively quickly using the specificinformation from the context engine 510 (for example, with respect tothe question “what is Alice holding?”, the context engine 510 hasalready identified which person is Alice, and that the object is a waterbottle). This relationship information output by the scene understandingengine 520 may enable the first user to ask otherwise ambiguousquestions such as “what is Alice holding?” or “who is the guy wearingthe blue hat?”, as well as commands such as “focus on the person next toAlice”. Further, the assistant system 140 may perform context carryoverin order to answer a chain of such ambiguous questions. For example, aviewing user may ask “who is behind Bob?”, and the assistant system 140may answer “Sarah is behind Bob”. Subsequently, the viewing user may ask“what is she looking at?”. The assistant system 140 may recognize thatthis question refers to the previously identified user “Sarah”, andrespond with “Sarah is looking at a cat”.

In particular embodiments, the assistant system 140 may send, to one ormore of the plurality of client systems 130, a response to the requestwhile maintaining the video call between the plurality of client systems130. In particular embodiments, which client systems(s) 130 the responseis sent to may be based on the intent of the request. As an example andnot by way of limitation, if the first user's request was forinformation about the scene 600 of the video call (e.g., “who isspeaking?”), or if the request was to virtually modify a characteristic(e.g., an appearance) of a second user, the response may be sent to thefirst client system 130 of the first user. As another example and not byway of limitation, if the first user's request was to share content withone or more second users, the identified content may be sent torespective client systems 130 of those one or more second users.Although this disclosure describes sending responses to client systems130 in a particular manner, this disclosure contemplates sendingresponses to client systems 130 in any suitable manner.

FIG. 7A illustrates an example shifted scene 700 viewed after a usercommand concerning an entity of the initial scene 600 on the firstclient system 130 of the first user 710. As an example and not by way oflimitation, a command by user 710 to “follow the cat” may cause scene600 to shift to the left as a smart camera on a client device of a userin the scene 600 tracks the cat to a different location, thus resultingin an updated scene 700. In particular embodiments, output of thecontext and scene understanding engines 510 and 520 may be dynamicallyupdated as the viewed scene 600 changes. FIG. 7B illustrates an exampleupdated chart 750 of information of the shifted scene 700 generated bythe context engine 510. As can be seen in chart 750, new entities nowvisible in the shifted scene 700 such as Dave and Eve have been added to“Social Presence” category 651, while objects fireplace, cat bed, andwine glass have been added to “Focal Object Recognition” category 653.FIG. 7C illustrates an example updated knowledge graph 760 of theshifted scene 700 generated by the scene understanding engine 520.Updated knowledge graph 760 of the shifted scene 700 may be generated bythe scene understanding engine 520 in response to a command containing awake-word concerning a relationship between entities detected by thecontext engine 510, such as “what is the cat carrying?”. Although theexample knowledge graphs 660 and 760 illustrate particular words as therelationship information expressed by the edges between nodesrepresenting entities and objects within a scene as determined by acontext engine 510, each relationship edge may be represented by varioussynonyms or related words. For example, the relationship information“holding” may also map to requests involving “carrying”, “lifting”, or“having”; similarly, “right of” and “left of” may be considered types ofa “next to” relationship.

FIG. 8 illustrates an example updated scene 800 viewed after a usercommand concerning an entity of a previous scene, such as scene 600, onthe first client system 130 of the first user 810. As an example and notby way of limitation, effects and animations may be incorporated intothe video call via the assistant system 140. User 810 may request thatthe assistant system 140 modify the appearance of a user (for example,upon noticing the cape-like quality of Carol's coat, user 810 mayrequest that the assistant system 140 add special effects such asvampire fangs or wings to user Carol), or alter the voice of the user(for example, using the voice of another person in the chat). As anotherexample and not by way of limitation, user 810 may request the assistantsystem 140 to add effects, such as a mask, onto another person, or toperform image processing to change the appearance of the other person(for example, making them appear to be younger or older than they are).The assistant system 140 may further incorporate AR/VR functionalitiesto allow the user 810 to request to, for example, give a user viewed inthe scene 800 a virtual hug.

In particular embodiments, this architecture, combined with relevantsocial-networking information, may enable the assistant system 140 todetermine the people and objects involved in the video call, thusenabling multiple social and sharing functions using theseidentifications. As an example and not by way of limitation, user 810may query the assistant system 140 as to where to buy the coat 611 thatCarol is wearing, and the assistant system 140 may respond with a linkto the appropriate product on an online shopping site.

FIG. 9 illustrates an example video call in which content relevant tothe video call is viewed on the client system 130 of a user 910. As anexample and not by way of limitation, during a video call 900, theassistant system 140 may provide content relevant to the video call orto a given user during a video call between users 910 and 920. Forinstance, the assistant system 140 may retrieve social-networkinginformation of user 920 during the video call, and provide thisinformation to a chatting user 910 to refer to during a conversation inthe video call. In particular embodiments, the assistant system 140 mayprovide a content sidebar 930 for display, such as by causing thecontent sidebar to pop up or slide into the frame of the video call.Content sidebar 930 may present various content items, such as photos931 and 932; these photos may be relevant to the two users 910 and 920or to a topic or property of the call. As an example and not by way oflimitation, photos 931 and 932 may be mutually tagged photos of theusers. Content sidebar 930 may also present content items such as video933; this video 933 may, similar to photos 931 and 932, involve users910 and/or 920, or it may be a video of a subject that concerns a commoninterest of both users 910 and 920. Content sidebar 930 may also displaysocial-networking content items, such as a post 934 that may be authoredby one of the users 910 and 920 or a by mutual contact, or that maysimply have one or more of the users 910 and 920 tagged in it. Contentsidebar 930 may further present social-networking information 935, suchas a list of mutual hobbies. Such content items 931-935 may increase thesense of social connection of the chatting users 910 and 920, and mayfurther guide their conversation, for example, when it appears that theymay be running out of topics to discuss.

Another technical challenge may be that, when conducting a video call onthe client device 130, the user of that device may wish to preserveaccess to the functions of the device and access to a smart assistantsystem, which may go dormant during the video call. A solution presentedby embodiments disclosed herein to address this challenge may thusinvolve the persistent assistant system 140 that, rather than goingdormant during a video call, remains active but on standby, and is thusaccessible to the first user to be invoked during the video call toexecute various commands on the client device 130 of the first user. Asanother example, providing the persistent, always-on assistant system140 may enable the first user to continue to use their client device 130and assistant system 140 normally, even while conducting the video call.

In particular embodiments, the assistant system 140 may perform varioussocial functions relating to users 910 and 920 during the video call. Asan example and not by way of limitation, user 910 may query theassistant system 140 as to what a topic of conversation should be for avideo call with another user 920, and, subject to privacy settings, theassistant system 140 may consult the social-networking information ofthat user 920 to identify common interests or a recent post created orshared by user 920. As another example and not by way of limitation, arequest to share a “[song/video/picture] with them” may result in therelevant media being shared with the other user(s) 920 in the videocall. In particular embodiments, the presence of the persistent,always-on assistant system 140 may enable user 910 to continue to usetheir client device 130 and assistant system 140 normally, even whileconducting a video call. As an example and not by way of limitation, theassistant system 140 may be queried as to what a particular person justsaid. As another example and not by way of limitation, the assistantsystem 140 may take notes during the video call, or even summarizeportions of or the entire call. As yet another example and not by way oflimitation, with the persistent assistant system 140 to executerequests, user device functions such as timers, weather alerts, alarms,news, and other utilities that may ordinarily be dormant during a videocall may remain accessible during that video call.

In particular embodiments, the persistent assistant system 140 and itsresulting single communication domain may further enable variouscommunication functionalities. As an example and not by way oflimitation, as discussed above, a smart microphone integrated with theassistant system 140 may allow a user to mute himself while speaking tothe assistant system 140, so that other users in the video call cannothear him. As another example and not by way of limitation, the assistantsystem 140 may translate between speakers (e.g., if another person inthe video call is speaking in Chinese, this speech may be translated inreal time into English, and provided to the user either in audio or ascaptions). As yet another example and not by way of limitation,accessibility functions may also be enabled, such as interpreting a gazeor gesture of a user as input, rather than an explicit audio command.

FIG. 10 illustrates an example method 1000 for generating a response toa user request to a persistent assistant system 140 made during a videocall. The method may begin at step 1010, where the persistent assistantsystem 140 may establish a video call between the client systems 130 ofmultiple users. At step 1020, the persistent assistant system 140 mayreceive a request from a first user of a first client system 130 to beperformed by the persistent assistant system 140 during the video call.At step 1030, the persistent assistant system 140 may determine useridentifiers of second users referenced in the request, as well asdetermining the intent of the request. For example, a user viewing ascene may command the persistent assistant system 140 to take a photo ofa second user within the scene with a camera, follow a certain user, orshow where a second user is located within the scene; the assistantsystem 140 may then determine the user identifier of this user in orderto perform the requested action. At step 1040, the assistant system 140may be instructed to execute the request based on the user identifiersand intent. At step 1050, the assistant system 140 may send a responseto the request to one or more of the second users while maintaining thevideo call. For example, if the request was to take a photo of a seconduser, the smart camera may zoom in on the identified second user andtake the requested photo, and then display that photo to one or moreparticipants on the video call during the actual video call.

Particular embodiments may repeat one or more steps of the method ofFIG. 10, where appropriate. Although this disclosure describes andillustrates particular steps of the method of FIG. 10 as occurring in aparticular order, this disclosure contemplates any suitable steps of themethod of FIG. 10 occurring in any suitable order. Moreover, althoughthis disclosure describes and illustrates an example method forgenerating a response to a user request made to a persistent assistantsystem during a video call including the particular steps of the methodof FIG. 10, this disclosure contemplates any suitable method forgenerating a response to a user request made to a persistent assistantsystem during a video call, including any suitable steps, which mayinclude all, some, or none of the steps of the method of FIG. 10, whereappropriate. Furthermore, although this disclosure describes andillustrates particular components, devices, or systems carrying outparticular steps of the method of FIG. 10, this disclosure contemplatesany suitable combination of any suitable components, devices, or systemscarrying out any suitable steps of the method of FIG. 10.

Social Graphs

FIG. 11 illustrates an example social graph 1100. In particularembodiments, the social-networking system 160 may store one or moresocial graphs 1100 in one or more data stores. In particularembodiments, the social graph 1100 may include multiple nodes—which mayinclude multiple user nodes 1102 or multiple concept nodes 1104—andmultiple edges 1106 connecting the nodes. Each node may be associatedwith a unique entity (i.e., user or concept), each of which may have aunique identifier (ID), such as a unique number or username. The examplesocial graph 1100 illustrated in FIG. 11 is shown, for didacticpurposes, in a two-dimensional visual map representation. In particularembodiments, a social-networking system 160, a client system 130, anassistant system 140, or a third-party system 170 may access the socialgraph 1100 and related social-graph information for suitableapplications. The nodes and edges of the social graph 1100 may be storedas data objects, for example, in a data store (such as a social-graphdatabase). Such a data store may include one or more searchable orqueryable indexes of nodes or edges of the social graph 1100.

In particular embodiments, a user node 1102 may correspond to a user ofthe social-networking system 160 or the assistant system 140. As anexample and not by way of limitation, a user may be an individual (humanuser), an entity (e.g., an enterprise, business, or third-partyapplication), or a group (e.g., of individuals or entities) thatinteracts or communicates with or over the social-networking system 160or the assistant system 140. In particular embodiments, when a userregisters for an account with the social-networking system 160, thesocial-networking system 160 may create a user node 1102 correspondingto the user, and store the user node 1102 in one or more data stores.Users and user nodes 1102 described herein may, where appropriate, referto registered users and user nodes 1102 associated with registeredusers. In addition or as an alternative, users and user nodes 1102described herein may, where appropriate, refer to users that have notregistered with the social-networking system 160. In particularembodiments, a user node 1102 may be associated with informationprovided by a user or information gathered by various systems, includingthe social-networking system 160. As an example and not by way oflimitation, a user may provide his or her name, profile picture, contactinformation, birth date, sex, marital status, family status, employment,education background, preferences, interests, or other demographicinformation. In particular embodiments, a user node 1102 may beassociated with one or more data objects corresponding to informationassociated with a user. In particular embodiments, a user node 1102 maycorrespond to one or more web interfaces.

In particular embodiments, a concept node 1104 may correspond to aconcept. As an example and not by way of limitation, a concept maycorrespond to a place (such as, for example, a movie theater,restaurant, landmark, or city); a website (such as, for example, awebsite associated with the social-networking system 160 or athird-party website associated with a web-application server); an entity(such as, for example, a person, business, group, sports team, orcelebrity); a resource (such as, for example, an audio file, video file,digital photo, text file, structured document, or application) which maybe located within the social-networking system 160 or on an externalserver, such as a web-application server; real or intellectual property(such as, for example, a sculpture, painting, movie, game, song, idea,photograph, or written work); a game; an activity; an idea or theory;another suitable concept; or two or more such concepts. A concept node1104 may be associated with information of a concept provided by a useror information gathered by various systems, including thesocial-networking system 160 and the assistant system 140. As an exampleand not by way of limitation, information of a concept may include aname or a title; one or more images (e.g., an image of the cover page ofa book); a location (e.g., an address or a geographical location); awebsite (which may be associated with a URL); contact information (e.g.,a phone number or an email address); other suitable concept information;or any suitable combination of such information. In particularembodiments, a concept node 1104 may be associated with one or more dataobjects corresponding to information associated with concept node 1104.In particular embodiments, a concept node 1104 may correspond to one ormore web interfaces.

In particular embodiments, a node in the social graph 1100 may representor be represented by a web interface (which may be referred to as a“profile interface”). Profile interfaces may be hosted by or accessibleto the social-networking system 160 or the assistant system 140. Profileinterfaces may also be hosted on third-party websites associated with athird-party system 170. As an example and not by way of limitation, aprofile interface corresponding to a particular external web interfacemay be the particular external web interface and the profile interfacemay correspond to a particular concept node 1104. Profile interfaces maybe viewable by all or a selected subset of other users. As an exampleand not by way of limitation, a user node 1102 may have a correspondinguser-profile interface in which the corresponding user may add content,make declarations, or otherwise express himself or herself. As anotherexample and not by way of limitation, a concept node 1104 may have acorresponding concept-profile interface in which one or more users mayadd content, make declarations, or express themselves, particularly inrelation to the concept corresponding to concept node 1104.

In particular embodiments, a concept node 1104 may represent athird-party web interface or resource hosted by a third-party system170. The third-party web interface or resource may include, among otherelements, content, a selectable or other icon, or other inter-actableobject representing an action or activity. As an example and not by wayof limitation, a third-party web interface may include a selectable iconsuch as “like,” “check-in,” “eat,” “recommend,” or another suitableaction or activity. A user viewing the third-party web interface mayperform an action by selecting one of the icons (e.g., “check-in”),causing a client system 130 to send to the social-networking system 160a message indicating the user's action. In response to the message, thesocial-networking system 160 may create an edge (e.g., a check-in-typeedge) between a user node 1102 corresponding to the user and a conceptnode 1104 corresponding to the third-party web interface or resource andstore edge 1106 in one or more data stores.

In particular embodiments, a pair of nodes in the social graph 1100 maybe connected to each other by one or more edges 1106. An edge 1106connecting a pair of nodes may represent a relationship between the pairof nodes. In particular embodiments, an edge 1106 may include orrepresent one or more data objects or attributes corresponding to therelationship between a pair of nodes. As an example and not by way oflimitation, a first user may indicate that a second user is a “friend”of the first user. In response to this indication, the social-networkingsystem 160 may send a “friend request” to the second user. If the seconduser confirms the “friend request,” the social-networking system 160 maycreate an edge 1106 connecting the first user's user node 1102 to thesecond user's user node 1102 in the social graph 1100 and store edge1106 as social-graph information in one or more of data stores 164. Inthe example of FIG. 11, the social graph 1100 includes an edge 1106indicating a friend relation between user nodes 1102 of user “A” anduser “B” and an edge indicating a friend relation between user nodes1102 of user “C” and user “B.” Although this disclosure describes orillustrates particular edges 1106 with particular attributes connectingparticular user nodes 1102, this disclosure contemplates any suitableedges 1106 with any suitable attributes connecting user nodes 1102. Asan example and not by way of limitation, an edge 1106 may represent afriendship, family relationship, business or employment relationship,fan relationship (including, e.g., liking, etc.), follower relationship,visitor relationship (including, e.g., accessing, viewing, checking-in,sharing, etc.), subscriber relationship, superior/subordinaterelationship, reciprocal relationship, non-reciprocal relationship,another suitable type of relationship, or two or more suchrelationships. Moreover, although this disclosure generally describesnodes as being connected, this disclosure also describes users orconcepts as being connected. Herein, references to users or conceptsbeing connected may, where appropriate, refer to the nodes correspondingto those users or concepts being connected in the social graph 1100 byone or more edges 1106. The degree of separation between two objectsrepresented by two nodes, respectively, is a count of edges in ashortest path connecting the two nodes in the social graph 1100. As anexample and not by way of limitation, in the social graph 1100, the usernode 1102 of user “C” is connected to the user node 1102 of user “A” viamultiple paths including, for example, a first path directly passingthrough the user node 1102 of user “B,” a second path passing throughthe concept node 1104 of company “CompanyName” and the user node 1102 ofuser “D,” and a third path passing through the user nodes 1102 andconcept nodes 1104 representing school “SchoolName,” user “G,” company“CompanyName,” and user “D.” User “C” and user “A” have a degree ofseparation of two because the shortest path connecting theircorresponding nodes (i.e., the first path) includes two edges 1106.

In particular embodiments, an edge 1106 between a user node 1102 and aconcept node 1104 may represent a particular action or activityperformed by a user associated with user node 1102 toward a conceptassociated with a concept node 1104. As an example and not by way oflimitation, as illustrated in FIG. 11, a user may “like,” “attended,”“played,” “listened,” “cooked,” “worked at,” or “read” a concept, eachof which may correspond to an edge type or subtype. A concept-profileinterface corresponding to a concept node 1104 may include, for example,a selectable “check in” icon (such as, for example, a clickable “checkin” icon) or a selectable “add to favorites” icon. Similarly, after auser clicks these icons, the social-networking system 160 may create a“favorite” edge or a “check in” edge in response to a user's actioncorresponding to a respective action. As another example and not by wayof limitation, a user (user “C”) may listen to a particular song(“SongName”) using a particular application (a third-party online musicapplication). In this case, the social-networking system 160 may createa “listened” edge 1106 and a “used” edge (as illustrated in FIG. 11)between user nodes 1102 corresponding to the user and concept nodes 1104corresponding to the song and application to indicate that the userlistened to the song and used the application. Moreover, thesocial-networking system 160 may create a “played” edge 1106 (asillustrated in FIG. 11) between concept nodes 1104 corresponding to thesong and the application to indicate that the particular song was playedby the particular application. In this case, “played” edge 1106corresponds to an action performed by an external application (thethird-party online music application) on an external audio file (thesong “SongName”). Although this disclosure describes particular edges1106 with particular attributes connecting user nodes 1102 and conceptnodes 1104, this disclosure contemplates any suitable edges 1106 withany suitable attributes connecting user nodes 1102 and concept nodes1104. Moreover, although this disclosure describes edges between a usernode 1102 and a concept node 1104 representing a single relationship,this disclosure contemplates edges between a user node 1102 and aconcept node 1104 representing one or more relationships. As an exampleand not by way of limitation, an edge 1106 may represent both that auser likes and has used at a particular concept. Alternatively, anotheredge 1106 may represent each type of relationship (or multiples of asingle relationship) between a user node 1102 and a concept node 1104(as illustrated in FIG. 11 between user node 1102 for user “E” andconcept node 1104 for “online music application”).

In particular embodiments, the social-networking system 160 may createan edge 1106 between a user node 1102 and a concept node 1104 in thesocial graph 1100. As an example and not by way of limitation, a userviewing a concept-profile interface (such as, for example, by using aweb browser or a special-purpose application hosted by the user's clientsystem 130) may indicate that he or she likes the concept represented bythe concept node 1104 by clicking or selecting a “Like” icon, which maycause the user's client system 130 to send to the social-networkingsystem 160 a message indicating the user's liking of the conceptassociated with the concept-profile interface. In response to themessage, the social-networking system 160 may create an edge 1106between user node 1102 associated with the user and concept node 1104,as illustrated by “like” edge 1106 between the user and concept node1104. In particular embodiments, the social-networking system 160 maystore an edge 1106 in one or more data stores. In particularembodiments, an edge 1106 may be automatically formed by thesocial-networking system 160 in response to a particular user action. Asan example and not by way of limitation, if a first user uploads apicture, reads a book, watches a movie, or listens to a song, an edge1106 may be formed between user node 1102 corresponding to the firstuser and concept nodes 1104 corresponding to those concepts. Althoughthis disclosure describes forming particular edges 1106 in particularmanners, this disclosure contemplates forming any suitable edges 1106 inany suitable manner.

Vector Spaces and Embeddings

FIG. 12 illustrates an example view of a vector space 1200. Inparticular embodiments, an object or an n-gram may be represented in ad-dimensional vector space, where d denotes any suitable number ofdimensions. Although the vector space 1200 is illustrated as athree-dimensional space, this is for illustrative purposes only, as thevector space 1200 may be of any suitable dimension. In particularembodiments, an n-gram may be represented in the vector space 1200 as avector referred to as a term embedding. Each vector may comprisecoordinates corresponding to a particular point in the vector space 1200(i.e., the terminal point of the vector). As an example and not by wayof limitation, vectors 1210, 1220, and 1230 may be represented as pointsin the vector space 1200, as illustrated in FIG. 12. An n-gram may bemapped to a respective vector representation. As an example and not byway of limitation, n-grams t₁ and t₂ may be mapped to vectors

and

in the vector space 1200, respectively, by applying a function

defined by a dictionary, such that

=

(t₁) and

=

(t₂). As another example and not by way of limitation, a dictionarytrained to map text to a vector representation may be utilized, or sucha dictionary may be itself generated via training. As another exampleand not by way of limitation, a word-embeddings model may be used to mapan n-gram to a vector representation in the vector space 1200. Inparticular embodiments, an n-gram may be mapped to a vectorrepresentation in the vector space 1200 by using a machine leaning model(e.g., a neural network). The machine learning model may have beentrained using a sequence of training data (e.g., a corpus of objectseach comprising n-grams).

In particular embodiments, an object may be represented in the vectorspace 1200 as a vector referred to as a feature vector or an objectembedding. As an example and not by way of limitation, objects e₁ and e₂may be mapped to vectors

and

in the vector space 1200, respectively, by applying a function

, such that

=

(e₁) and

=

(e₂). In particular embodiments, an object may be mapped to a vectorbased on one or more properties, attributes, or features of the object,relationships of the object with other objects, or any other suitableinformation associated with the object. As an example and not by way oflimitation, a function

may map objects to vectors by feature extraction, which may start froman initial set of measured data and build derived values (e.g.,features). As an example and not by way of limitation, an objectcomprising a video or an image may be mapped to a vector by using analgorithm to detect or isolate various desired portions or shapes of theobject. Features used to calculate the vector may be based oninformation obtained from edge detection, corner detection, blobdetection, ridge detection, scale-invariant feature transformation, edgedirection, changing intensity, autocorrelation, motion detection,optical flow, thresholding, blob extraction, template matching, Houghtransformation (e.g., lines, circles, ellipses, arbitrary shapes), orany other suitable information. As another example and not by way oflimitation, an object comprising audio data may be mapped to a vectorbased on features such as a spectral slope, a tonality coefficient, anaudio spectrum centroid, an audio spectrum envelope, a Mel-frequencycepstrum, or any other suitable information. In particular embodiments,when an object has data that is either too large to be efficientlyprocessed or comprises redundant data, a function

may map the object to a vector using a transformed reduced set offeatures (e.g., feature selection). In particular embodiments, afunction

may map an object e to a vector

(e) based on one or more n-grams associated with object e. Although thisdisclosure describes representing an n-gram or an object in a vectorspace in a particular manner, this disclosure contemplates representingan n-gram or an object in a vector space in any suitable manner.

In particular embodiments, the social-networking system 160 maycalculate a similarity metric of vectors in vector space 1200. Asimilarity metric may be a cosine similarity, a Minkowski distance, aMahalanobis distance, a Jaccard similarity coefficient, or any suitablesimilarity metric. As an example and not by way of limitation, asimilarity metric of

and

may be a cosine similarity

$\frac{\overset{harpoonup}{v_{1}} \cdot \overset{harpoonup}{v_{2}}}{{\overset{harpoonup}{v_{1}}}{\overset{harpoonup}{v_{2}}}}.$

As another example and not by way of limitation, a similarity metric of

and

may be a Euclidean distance ∥

−

∥. A similarity metric of two vectors may represent how similar the twoobjects or n-grams corresponding to the two vectors, respectively, areto one another, as measured by the distance between the two vectors inthe vector space 1200. As an example and not by way of limitation,vector 1210 and vector 1220 may correspond to objects that are moresimilar to one another than the objects corresponding to vector 1210 andvector 1230, based on the distance between the respective vectors.Although this disclosure describes calculating a similarity metricbetween vectors in a particular manner, this disclosure contemplatescalculating a similarity metric between vectors in any suitable manner.

More information on vector spaces, embeddings, feature vectors, andsimilarity metrics may be found in U.S. patent application Ser. No.14/949,436, filed 23 Nov. 2015, U.S. patent application Ser. No.15/286,315, filed 5 Oct. 2016, and U.S. patent application Ser. No.15/365,789, filed 30 Nov. 2016, each of which is incorporated byreference.

Artificial Neural Networks

FIG. 13 illustrates an example artificial neural network (“ANN”) 1300.In particular embodiments, an ANN may refer to a computational modelcomprising one or more nodes. Example ANN 1300 may comprise an inputlayer 1310, hidden layers 1320, 1330, 1340, and an output layer 1350.Each layer of the ANN 1300 may comprise one or more nodes, such as anode 1305 or a node 1315. In particular embodiments, each node of an ANNmay be connected to another node of the ANN. As an example and not byway of limitation, each node of the input layer 1310 may be connected toone of more nodes of the hidden layer 1320. In particular embodiments,one or more nodes may be a bias node (e.g., a node in a layer that isnot connected to and does not receive input from any node in a previouslayer). In particular embodiments, each node in each layer may beconnected to one or more nodes of a previous or subsequent layer.Although FIG. 13 depicts a particular ANN with a particular number oflayers, a particular number of nodes, and particular connections betweennodes, this disclosure contemplates any suitable ANN with any suitablenumber of layers, any suitable number of nodes, and any suitableconnections between nodes. As an example and not by way of limitation,although FIG. 13 depicts a connection between each node of the inputlayer 1310 and each node of the hidden layer 1320, one or more nodes ofthe input layer 1310 may not be connected to one or more nodes of thehidden layer 1320.

In particular embodiments, an ANN may be a feedforward ANN (e.g., an ANNwith no cycles or loops where communication between nodes flows in onedirection beginning with the input layer and proceeding to successivelayers). As an example and not by way of limitation, the input to eachnode of the hidden layer 1320 may comprise the output of one or morenodes of the input layer 1310. As another example and not by way oflimitation, the input to each node of the output layer 1350 may comprisethe output of one or more nodes of the hidden layer 1340. In particularembodiments, an ANN may be a deep neural network (e.g., a neural networkcomprising at least two hidden layers). In particular embodiments, anANN may be a deep residual network. A deep residual network may be afeedforward ANN comprising hidden layers organized into residual blocks.The input into each residual block after the first residual block may bea function of the output of the previous residual block and the input ofthe previous residual block. As an example and not by way of limitation,the input into residual block N may be F(x)+x, where F(x) may be theoutput of residual block N−1, x may be the input into residual blockN−1. Although this disclosure describes a particular ANN, thisdisclosure contemplates any suitable ANN.

In particular embodiments, an activation function may correspond to eachnode of an ANN. An activation function of a node may define the outputof a node for a given input. In particular embodiments, an input to anode may comprise a set of inputs. As an example and not by way oflimitation, an activation function may be an identity function, a binarystep function, a logistic function, or any other suitable function. Asanother example and not by way of limitation, an activation function fora node k may be the sigmoid function

${{F_{k}( s_{k} )} = \frac{1}{1 + e^{- s_{k}}}},$

the hyperbolic tangent function

${{F_{k}( s_{k} )} = \frac{e^{s_{k}} - e^{- s_{k}}}{e^{s_{k}} + e^{- s_{k}}}},$

the rectifier F_(k)(s_(k))=max (0, s_(k)), or any other suitablefunction F_(k)(s_(k)), where s_(k) may be the effective input to node k.In particular embodiments, the input of an activation functioncorresponding to a node may be weighted. Each node may generate outputusing a corresponding activation function based on weighted inputs. Inparticular embodiments, each connection between nodes may be associatedwith a weight. As an example and not by way of limitation, a connection1325 between the node 1305 and the node 1315 may have a weightingcoefficient of 0.4, which may indicate that 0.4 multiplied by the outputof the node 1305 is used as an input to the node 1315. As anotherexample and not by way of limitation, the output y_(k) of node k may bey_(k)=F_(k)(s_(k)), where F_(k) may be the activation functioncorresponding to node k, s_(k)=Σ_(j)(w_(jk)x_(j)) may be the effectiveinput to node k, x₁ may be the output of a node j connected to node k,and w_(jk) may be the weighting coefficient between node j and node k.In particular embodiments, the input to nodes of the input layer may bebased on a vector representing an object. Although this disclosuredescribes particular inputs to and outputs of nodes, this disclosurecontemplates any suitable inputs to and outputs of nodes. Moreover,although this disclosure may describe particular connections and weightsbetween nodes, this disclosure contemplates any suitable connections andweights between nodes.

In particular embodiments, an ANN may be trained using training data. Asan example and not by way of limitation, training data may compriseinputs to the ANN 1300 and an expected output. As another example andnot by way of limitation, training data may comprise vectors eachrepresenting a training object and an expected label for each trainingobject. In particular embodiments, training an ANN may comprisemodifying the weights associated with the connections between nodes ofthe ANN by optimizing an objective function. As an example and not byway of limitation, a training method may be used (e.g., the conjugategradient method, the gradient descent method, the stochastic gradientdescent) to backpropagate the sum-of-squares error measured as adistances between each vector representing a training object (e.g.,using a cost function that minimizes the sum-of-squares error). Inparticular embodiments, an ANN may be trained using a dropout technique.As an example and not by way of limitation, one or more nodes may betemporarily omitted (e.g., receive no input and generate no output)while training. For each training object, one or more nodes of the ANNmay have some probability of being omitted. The nodes that are omittedfor a particular training object may be different than the nodes omittedfor other training objects (e.g., the nodes may be temporarily omittedon an object-by-object basis). Although this disclosure describestraining an ANN in a particular manner, this disclosure contemplatestraining an ANN in any suitable manner.

Privacy

In particular embodiments, one or more objects (e.g., content or othertypes of objects) of a computing system may be associated with one ormore privacy settings. The one or more objects may be stored on orotherwise associated with any suitable computing system or application,such as, for example, a social-networking system 160, a client system130, an assistant system 140, a third-party system 170, asocial-networking application, an assistant application, a messagingapplication, a photo-sharing application, or any other suitablecomputing system or application. Although the examples discussed hereinare in the context of an online social network, these privacy settingsmay be applied to any other suitable computing system. Privacy settings(or “access settings”) for an object may be stored in any suitablemanner, such as, for example, in association with the object, in anindex on an authorization server, in another suitable manner, or anysuitable combination thereof. A privacy setting for an object mayspecify how the object (or particular information associated with theobject) can be accessed, stored, or otherwise used (e.g., viewed,shared, modified, copied, executed, surfaced, or identified) within theonline social network. When privacy settings for an object allow aparticular user or other entity to access that object, the object may bedescribed as being “visible” with respect to that user or other entity.As an example and not by way of limitation, a user of the online socialnetwork may specify privacy settings for a user-profile page thatidentify a set of users that may access work-experience information onthe user-profile page, thus excluding other users from accessing thatinformation.

In particular embodiments, privacy settings for an object may specify a“blocked list” of users or other entities that should not be allowed toaccess certain information associated with the object. In particularembodiments, the blocked list may include third-party entities. Theblocked list may specify one or more users or entities for which anobject is not visible. As an example and not by way of limitation, auser may specify a set of users who may not access photo albumsassociated with the user, thus excluding those users from accessing thephoto albums (while also possibly allowing certain users not within thespecified set of users to access the photo albums). In particularembodiments, privacy settings may be associated with particularsocial-graph elements. Privacy settings of a social-graph element, suchas a node or an edge, may specify how the social-graph element,information associated with the social-graph element, or objectsassociated with the social-graph element can be accessed using theonline social network. As an example and not by way of limitation, aparticular concept node 1104 corresponding to a particular photo mayhave a privacy setting specifying that the photo may be accessed only byusers tagged in the photo and friends of the users tagged in the photo.In particular embodiments, privacy settings may allow users to opt in toor opt out of having their content, information, or actionsstored/logged by the social-networking system 160 or assistant system140 or shared with other systems (e.g., a third-party system 170).Although this disclosure describes using particular privacy settings ina particular manner, this disclosure contemplates using any suitableprivacy settings in any suitable manner.

In particular embodiments, privacy settings may be based on one or morenodes or edges of a social graph 1100. A privacy setting may bespecified for one or more edges 1106 or edge-types of the social graph1100, or with respect to one or more nodes 1102, 1104 or node-types ofthe social graph 1100. The privacy settings applied to a particular edge1106 connecting two nodes may control whether the relationship betweenthe two entities corresponding to the nodes is visible to other users ofthe online social network. Similarly, the privacy settings applied to aparticular node may control whether the user or concept corresponding tothe node is visible to other users of the online social network. As anexample and not by way of limitation, a first user may share an objectto the social-networking system 160. The object may be associated with aconcept node 1104 connected to a user node 1102 of the first user by anedge 1106. The first user may specify privacy settings that apply to aparticular edge 1106 connecting to the concept node 1104 of the object,or may specify privacy settings that apply to all edges 1106 connectingto the concept node 1104. As another example and not by way oflimitation, the first user may share a set of objects of a particularobject-type (e.g., a set of images). The first user may specify privacysettings with respect to all objects associated with the first user ofthat particular object-type as having a particular privacy setting(e.g., specifying that all images posted by the first user are visibleonly to friends of the first user and/or users tagged in the images).

In particular embodiments, the social-networking system 160 may presenta “privacy wizard” (e.g., within a webpage, a module, one or more dialogboxes, or any other suitable interface) to the first user to assist thefirst user in specifying one or more privacy settings. The privacywizard may display instructions, suitable privacy-related information,current privacy settings, one or more input fields for accepting one ormore inputs from the first user specifying a change or confirmation ofprivacy settings, or any suitable combination thereof. In particularembodiments, the social-networking system 160 may offer a “dashboard”functionality to the first user that may display, to the first user,current privacy settings of the first user. The dashboard functionalitymay be displayed to the first user at any appropriate time (e.g.,following an input from the first user summoning the dashboardfunctionality, following the occurrence of a particular event or triggeraction). The dashboard functionality may allow the first user to modifyone or more of the first user's current privacy settings at any time, inany suitable manner (e.g., redirecting the first user to the privacywizard).

Privacy settings associated with an object may specify any suitablegranularity of permitted access or denial of access. As an example andnot by way of limitation, access or denial of access may be specifiedfor particular users (e.g., only me, my roommates, my boss), userswithin a particular degree-of-separation (e.g., friends,friends-of-friends), user groups (e.g., the gaming club, my family),user networks (e.g., employees of particular employers, students oralumni of particular university), all users (“public”), no users(“private”), users of third-party systems 170, particular applications(e.g., third-party applications, external websites), other suitableentities, or any suitable combination thereof. Although this disclosuredescribes particular granularities of permitted access or denial ofaccess, this disclosure contemplates any suitable granularities ofpermitted access or denial of access.

In particular embodiments, one or more servers 162 may beauthorization/privacy servers for enforcing privacy settings. Inresponse to a request from a user (or other entity) for a particularobject stored in a data store 164, the social-networking system 160 maysend a request to the data store 164 for the object. The request mayidentify the user associated with the request and the object may be sentonly to the user (or a client system 130 of the user) if theauthorization server determines that the user is authorized to accessthe object based on the privacy settings associated with the object. Ifthe requesting user is not authorized to access the object, theauthorization server may prevent the requested object from beingretrieved from the data store 164 or may prevent the requested objectfrom being sent to the user. In the search-query context, an object maybe provided as a search result only if the querying user is authorizedto access the object, e.g., if the privacy settings for the object allowit to be surfaced to, discovered by, or otherwise visible to thequerying user. In particular embodiments, an object may representcontent that is visible to a user through a newsfeed of the user. As anexample and not by way of limitation, one or more objects may be visibleto a user's “Trending” page. In particular embodiments, an object maycorrespond to a particular user. The object may be content associatedwith the particular user, or may be the particular user's account orinformation stored on the social-networking system 160, or othercomputing system. As an example and not by way of limitation, a firstuser may view one or more second users of an online social networkthrough a “People You May Know” function of the online social network,or by viewing a list of friends of the first user. As an example and notby way of limitation, a first user may specify that they do not wish tosee objects associated with a particular second user in their newsfeedor friends list. If the privacy settings for the object do not allow itto be surfaced to, discovered by, or visible to the user, the object maybe excluded from the search results. Although this disclosure describesenforcing privacy settings in a particular manner, this disclosurecontemplates enforcing privacy settings in any suitable manner.

In particular embodiments, different objects of the same type associatedwith a user may have different privacy settings. Different types ofobjects associated with a user may have different types of privacysettings. As an example and not by way of limitation, a first user mayspecify that the first user's status updates are public, but any imagesshared by the first user are visible only to the first user's friends onthe online social network. As another example and not by way oflimitation, a user may specify different privacy settings for differenttypes of entities, such as individual users, friends-of-friends,followers, user groups, or corporate entities. As another example andnot by way of limitation, a first user may specify a group of users thatmay view videos posted by the first user, while keeping the videos frombeing visible to the first user's employer. In particular embodiments,different privacy settings may be provided for different user groups oruser demographics. As an example and not by way of limitation, a firstuser may specify that other users who attend the same university as thefirst user may view the first user's pictures, but that other users whoare family members of the first user may not view those same pictures.

In particular embodiments, the social-networking system 160 may provideone or more default privacy settings for each object of a particularobject-type. A privacy setting for an object that is set to a defaultmay be changed by a user associated with that object. As an example andnot by way of limitation, all images posted by a first user may have adefault privacy setting of being visible only to friends of the firstuser and, for a particular image, the first user may change the privacysetting for the image to be visible to friends and friends-of-friends.

In particular embodiments, privacy settings may allow a first user tospecify (e.g., by opting out, by not opting in) whether thesocial-networking system 160 or assistant system 140 may receive,collect, log, or store particular objects or information associated withthe user for any purpose. In particular embodiments, privacy settingsmay allow the first user to specify whether particular applications orprocesses may access, store, or use particular objects or informationassociated with the user. The privacy settings may allow the first userto opt in or opt out of having objects or information accessed, stored,or used by specific applications or processes. The social-networkingsystem 160 or assistant system 140 may access such information in orderto provide a particular function or service to the first user, withoutthe social-networking system 160 or assistant system 140 having accessto that information for any other purposes. Before accessing, storing,or using such objects or information, the social-networking system 160or assistant system 140 may prompt the user to provide privacy settingsspecifying which applications or processes, if any, may access, store,or use the object or information prior to allowing any such action. Asan example and not by way of limitation, a first user may transmit amessage to a second user via an application related to the online socialnetwork (e.g., a messaging app), and may specify privacy settings thatsuch messages should not be stored by the social-networking system 160or assistant system 140.

In particular embodiments, a user may specify whether particular typesof objects or information associated with the first user may beaccessed, stored, or used by the social-networking system 160 orassistant system 140. As an example and not by way of limitation, thefirst user may specify that images sent by the first user through thesocial-networking system 160 or assistant system 140 may not be storedby the social-networking system 160 or assistant system 140. As anotherexample and not by way of limitation, a first user may specify thatmessages sent from the first user to a particular second user may not bestored by the social-networking system 160 or assistant system 140. Asyet another example and not by way of limitation, a first user mayspecify that all objects sent via a particular application may be savedby the social-networking system 160 or assistant system 140.

In particular embodiments, privacy settings may allow a first user tospecify whether particular objects or information associated with thefirst user may be accessed from particular client systems 130 orthird-party systems 170. The privacy settings may allow the first userto opt in or opt out of having objects or information accessed from aparticular device (e.g., the phone book on a user's smart phone), from aparticular application (e.g., a messaging app), or from a particularsystem (e.g., an email server). The social-networking system 160 orassistant system 140 may provide default privacy settings with respectto each device, system, or application, and/or the first user may beprompted to specify a particular privacy setting for each context. As anexample and not by way of limitation, the first user may utilize alocation-services feature of the social-networking system 160 orassistant system 140 to provide recommendations for restaurants or otherplaces in proximity to the user. The first user's default privacysettings may specify that the social-networking system 160 or assistantsystem 140 may use location information provided from a client device130 of the first user to provide the location-based services, but thatthe social-networking system 160 or assistant system 140 may not storethe location information of the first user or provide it to anythird-party system 170. The first user may then update the privacysettings to allow location information to be used by a third-partyimage-sharing application in order to geo-tag photos.

In particular embodiments, privacy settings may allow a user to specifyone or more geographic locations from which objects can be accessed.Access or denial of access to the objects may depend on the geographiclocation of a user who is attempting to access the objects. As anexample and not by way of limitation, a user may share an object andspecify that only users in the same city may access or view the object.As another example and not by way of limitation, a first user may sharean object and specify that the object is visible to second users onlywhile the first user is in a particular location. If the first userleaves the particular location, the object may no longer be visible tothe second users. As another example and not by way of limitation, afirst user may specify that an object is visible only to second userswithin a threshold distance from the first user. If the first usersubsequently changes location, the original second users with access tothe object may lose access, while a new group of second users may gainaccess as they come within the threshold distance of the first user.

In particular embodiments, the social-networking system 160 or assistantsystem 140 may have functionalities that may use, as inputs, personal orbiometric information of a user for user-authentication orexperience-personalization purposes. A user may opt to make use of thesefunctionalities to enhance their experience on the online socialnetwork. As an example and not by way of limitation, a user may providepersonal or biometric information to the social-networking system 160 orassistant system 140. The user's privacy settings may specify that suchinformation may be used only for particular processes, such asauthentication, and further specify that such information may not beshared with any third-party system 170 or used for other processes orapplications associated with the social-networking system 160 orassistant system 140. As another example and not by way of limitation,the social-networking system 160 may provide a functionality for a userto provide voice-print recordings to the online social network. As anexample and not by way of limitation, if a user wishes to utilize thisfunction of the online social network, the user may provide a voicerecording of his or her own voice to provide a status update on theonline social network. The recording of the voice-input may be comparedto a voice print of the user to determine what words were spoken by theuser. The user's privacy setting may specify that such voice recordingmay be used only for voice-input purposes (e.g., to authenticate theuser, to send voice messages, to improve voice recognition in order touse voice-operated features of the online social network), and furtherspecify that such voice recording may not be shared with any third-partysystem 170 or used by other processes or applications associated withthe social-networking system 160. As another example and not by way oflimitation, the social-networking system 160 may provide a functionalityfor a user to provide a reference image (e.g., a facial profile, aretinal scan) to the online social network. The online social networkmay compare the reference image against a later-received image input(e.g., to authenticate the user, to tag the user in photos). The user'sprivacy setting may specify that such image may be used only for alimited purpose (e.g., authentication, tagging the user in photos), andfurther specify that such image may not be shared with any third-partysystem 170 or used by other processes or applications associated withthe social-networking system 160.

Systems and Methods

FIG. 14 illustrates an example computer system 1400. In particularembodiments, one or more computer systems 1400 perform one or more stepsof one or more methods described or illustrated herein. In particularembodiments, one or more computer systems 1400 provide functionalitydescribed or illustrated herein. In particular embodiments, softwarerunning on one or more computer systems 1400 performs one or more stepsof one or more methods described or illustrated herein or providesfunctionality described or illustrated herein. Particular embodimentsinclude one or more portions of one or more computer systems 1400.Herein, reference to a computer system may encompass a computing device,and vice versa, where appropriate. Moreover, reference to a computersystem may encompass one or more computer systems, where appropriate.

This disclosure contemplates any suitable number of computer systems1400. This disclosure contemplates computer system 1400 taking anysuitable physical form. As example and not by way of limitation,computer system 1400 may be an embedded computer system, asystem-on-chip (SOC), a single-board computer system (SBC) (such as, forexample, a computer-on-module (COM) or system-on-module (SOM)), adesktop computer system, a laptop or notebook computer system, aninteractive kiosk, a mainframe, a mesh of computer systems, a mobiletelephone, a personal digital assistant (PDA), a server, a tabletcomputer system, or a combination of two or more of these. Whereappropriate, computer system 1400 may include one or more computersystems 1400; be unitary or distributed; span multiple locations; spanmultiple machines; span multiple data centers; or reside in a cloud,which may include one or more cloud components in one or more networks.Where appropriate, one or more computer systems 1400 may perform withoutsubstantial spatial or temporal limitation one or more steps of one ormore methods described or illustrated herein. As an example and not byway of limitation, one or more computer systems 1400 may perform in realtime or in batch mode one or more steps of one or more methods describedor illustrated herein. One or more computer systems 1400 may perform atdifferent times or at different locations one or more steps of one ormore methods described or illustrated herein, where appropriate.

In particular embodiments, computer system 1400 includes a processor1402, memory 1404, storage 1406, an input/output (I/O) interface 1408, acommunication interface 1410, and a bus 1412. Although this disclosuredescribes and illustrates a particular computer system having aparticular number of particular components in a particular arrangement,this disclosure contemplates any suitable computer system having anysuitable number of any suitable components in any suitable arrangement.

In particular embodiments, processor 1402 includes hardware forexecuting instructions, such as those making up a computer program. Asan example and not by way of limitation, to execute instructions,processor 1402 may retrieve (or fetch) the instructions from an internalregister, an internal cache, memory 1404, or storage 1406; decode andexecute them; and then write one or more results to an internalregister, an internal cache, memory 1404, or storage 1406. In particularembodiments, processor 1402 may include one or more internal caches fordata, instructions, or addresses. This disclosure contemplates processor1402 including any suitable number of any suitable internal caches,where appropriate. As an example and not by way of limitation, processor1402 may include one or more instruction caches, one or more datacaches, and one or more translation lookaside buffers (TLBs).Instructions in the instruction caches may be copies of instructions inmemory 1404 or storage 1406, and the instruction caches may speed upretrieval of those instructions by processor 1402. Data in the datacaches may be copies of data in memory 1404 or storage 1406 forinstructions executing at processor 1402 to operate on; the results ofprevious instructions executed at processor 1402 for access bysubsequent instructions executing at processor 1402 or for writing tomemory 1404 or storage 1406; or other suitable data. The data caches mayspeed up read or write operations by processor 1402. The TLBs may speedup virtual-address translation for processor 1402. In particularembodiments, processor 1402 may include one or more internal registersfor data, instructions, or addresses. This disclosure contemplatesprocessor 1402 including any suitable number of any suitable internalregisters, where appropriate. Where appropriate, processor 1402 mayinclude one or more arithmetic logic units (ALUs); be a multi-coreprocessor; or include one or more processors 1402. Although thisdisclosure describes and illustrates a particular processor, thisdisclosure contemplates any suitable processor.

In particular embodiments, memory 1404 includes main memory for storinginstructions for processor 1402 to execute or data for processor 1402 tooperate on. As an example and not by way of limitation, computer system1400 may load instructions from storage 1406 or another source (such as,for example, another computer system 1400) to memory 1404. Processor1402 may then load the instructions from memory 1404 to an internalregister or internal cache. To execute the instructions, processor 1402may retrieve the instructions from the internal register or internalcache and decode them. During or after execution of the instructions,processor 1402 may write one or more results (which may be intermediateor final results) to the internal register or internal cache. Processor1402 may then write one or more of those results to memory 1404. Inparticular embodiments, processor 1402 executes only instructions in oneor more internal registers or internal caches or in memory 1404 (asopposed to storage 1406 or elsewhere) and operates only on data in oneor more internal registers or internal caches or in memory 1404 (asopposed to storage 1406 or elsewhere). One or more memory buses (whichmay each include an address bus and a data bus) may couple processor1402 to memory 1404. Bus 1412 may include one or more memory buses, asdescribed below. In particular embodiments, one or more memorymanagement units (MMUs) reside between processor 1402 and memory 1404and facilitate accesses to memory 1404 requested by processor 1402. Inparticular embodiments, memory 1404 includes random access memory (RAM).This RAM may be volatile memory, where appropriate. Where appropriate,this RAM may be dynamic RAM (DRAM) or static RAM (SRAM). Moreover, whereappropriate, this RAM may be single-ported or multi-ported RAM. Thisdisclosure contemplates any suitable RAM. Memory 1404 may include one ormore memories 1404, where appropriate. Although this disclosuredescribes and illustrates particular memory, this disclosurecontemplates any suitable memory.

In particular embodiments, storage 1406 includes mass storage for dataor instructions. As an example and not by way of limitation, storage1406 may include a hard disk drive (HDD), a floppy disk drive, flashmemory, an optical disc, a magneto-optical disc, magnetic tape, or aUniversal Serial Bus (USB) drive or a combination of two or more ofthese. Storage 1406 may include removable or non-removable (or fixed)media, where appropriate. Storage 1406 may be internal or external tocomputer system 1400, where appropriate. In particular embodiments,storage 1406 is non-volatile, solid-state memory. In particularembodiments, storage 1406 includes read-only memory (ROM). Whereappropriate, this ROM may be mask-programmed ROM, programmable ROM(PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM),electrically alterable ROM (EAROM), or flash memory or a combination oftwo or more of these. This disclosure contemplates mass storage 1406taking any suitable physical form. Storage 1406 may include one or morestorage control units facilitating communication between processor 1402and storage 1406, where appropriate. Where appropriate, storage 1406 mayinclude one or more storages 1406. Although this disclosure describesand illustrates particular storage, this disclosure contemplates anysuitable storage.

In particular embodiments, I/O interface 1408 includes hardware,software, or both, providing one or more interfaces for communicationbetween computer system 1400 and one or more I/O devices. Computersystem 1400 may include one or more of these I/O devices, whereappropriate. One or more of these I/O devices may enable communicationbetween a person and computer system 1400. As an example and not by wayof limitation, an I/O device may include a keyboard, keypad, microphone,monitor, mouse, printer, scanner, speaker, still camera, stylus, tablet,touch screen, trackball, video camera, another suitable I/O device or acombination of two or more of these. An I/O device may include one ormore sensors. This disclosure contemplates any suitable I/O devices andany suitable I/O interfaces 1408 for them. Where appropriate, I/Ointerface 1408 may include one or more device or software driversenabling processor 1402 to drive one or more of these I/O devices. I/Ointerface 1408 may include one or more I/O interfaces 1408, whereappropriate. Although this disclosure describes and illustrates aparticular I/O interface, this disclosure contemplates any suitable I/Ointerface.

In particular embodiments, communication interface 1410 includeshardware, software, or both providing one or more interfaces forcommunication (such as, for example, packet-based communication) betweencomputer system 1400 and one or more other computer systems 1400 or oneor more networks. As an example and not by way of limitation,communication interface 1410 may include a network interface controller(NIC) or network adapter for communicating with an Ethernet or otherwire-based network or a wireless NIC (WNIC) or wireless adapter forcommunicating with a wireless network, such as a WI-FI network. Thisdisclosure contemplates any suitable network and any suitablecommunication interface 1410 for it. As an example and not by way oflimitation, computer system 1400 may communicate with an ad hoc network,a personal area network (PAN), a local area network (LAN), a wide areanetwork (WAN), a metropolitan area network (MAN), or one or moreportions of the Internet or a combination of two or more of these. Oneor more portions of one or more of these networks may be wired orwireless. As an example, computer system 1400 may communicate with awireless PAN (WPAN) (such as, for example, a BLUETOOTH WPAN), a WI-FInetwork, a WI-MAX network, a cellular telephone network (such as, forexample, a Global System for Mobile Communications (GSM) network), orother suitable wireless network or a combination of two or more ofthese. Computer system 1400 may include any suitable communicationinterface 1410 for any of these networks, where appropriate.Communication interface 1410 may include one or more communicationinterfaces 1410, where appropriate. Although this disclosure describesand illustrates a particular communication interface, this disclosurecontemplates any suitable communication interface.

In particular embodiments, bus 1412 includes hardware, software, or bothcoupling components of computer system 1400 to each other. As an exampleand not by way of limitation, bus 1412 may include an AcceleratedGraphics Port (AGP) or other graphics bus, an Enhanced Industry StandardArchitecture (EISA) bus, a front-side bus (FSB), a HYPERTRANSPORT (HT)interconnect, an Industry Standard Architecture (ISA) bus, an INFINIBANDinterconnect, a low-pin-count (LPC) bus, a memory bus, a Micro ChannelArchitecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, aPCI-Express (PCIe) bus, a serial advanced technology attachment (SATA)bus, a Video Electronics Standards Association local (VLB) bus, oranother suitable bus or a combination of two or more of these. Bus 1412may include one or more buses 1412, where appropriate. Although thisdisclosure describes and illustrates a particular bus, this disclosurecontemplates any suitable bus or interconnect.

Herein, a computer-readable non-transitory storage medium or media mayinclude one or more semiconductor-based or other integrated circuits(ICs) (such, as for example, field-programmable gate arrays (FPGAs) orapplication-specific ICs (ASICs)), hard disk drives (HDDs), hybrid harddrives (HHDs), optical discs, optical disc drives (ODDs),magneto-optical discs, magneto-optical drives, floppy diskettes, floppydisk drives (FDDs), magnetic tapes, solid-state drives (SSDs),RAM-drives, SECURE DIGITAL cards or drives, any other suitablecomputer-readable non-transitory storage media, or any suitablecombination of two or more of these, where appropriate. Acomputer-readable non-transitory storage medium may be volatile,non-volatile, or a combination of volatile and non-volatile, whereappropriate.

Miscellaneous

Herein, “or” is inclusive and not exclusive, unless expressly indicatedotherwise or indicated otherwise by context. Therefore, herein, “A or B”means “A, B, or both,” unless expressly indicated otherwise or indicatedotherwise by context. Moreover, “and” is both joint and several, unlessexpressly indicated otherwise or indicated otherwise by context.Therefore, herein, “A and B” means “A and B, jointly or severally,”unless expressly indicated otherwise or indicated otherwise by context.

The scope of this disclosure encompasses all changes, substitutions,variations, alterations, and modifications to the example embodimentsdescribed or illustrated herein that a person having ordinary skill inthe art would comprehend. The scope of this disclosure is not limited tothe example embodiments described or illustrated herein. Moreover,although this disclosure describes and illustrates respectiveembodiments herein as including particular components, elements,feature, functions, operations, or steps, any of these embodiments mayinclude any combination or permutation of any of the components,elements, features, functions, operations, or steps described orillustrated anywhere herein that a person having ordinary skill in theart would comprehend. Furthermore, reference in the appended claims toan apparatus or system or a component of an apparatus or system beingadapted to, arranged to, capable of, configured to, enabled to, operableto, or operative to perform a particular function encompasses thatapparatus, system, component, whether or not it or that particularfunction is activated, turned on, or unlocked, as long as thatapparatus, system, or component is so adapted, arranged, capable,configured, enabled, operable, or operative. Additionally, although thisdisclosure describes or illustrates particular embodiments as providingparticular advantages, particular embodiments may provide none, some, orall of these advantages.

What is claimed is:
 1. A method comprising, by one or more computingsystems: establishing a video call between a plurality of clientsystems, wherein access to an assistant system is persistentlymaintained during the video call; receiving, from a first client systemof the plurality of client systems, a request by a first user to beperformed by the assistant system during the video call, wherein therequest references one or more activities associated with one or moreusers associated with the plurality of client systems; analyzing, by acontext engine of the assistant system, images of a scene of the videocall to identify the one or more activities within the scene;instructing the assistant system to execute the request based on theidentified one or more activities; and sending, to one or more of theplurality of client systems, a response to the request while maintainingthe video call between the plurality of client systems.
 2. The method ofclaim 1, wherein the request by the first user further references aninstruction to perform a virtual activity with respect to one or more ofthe activities.
 3. The method of claim 1, wherein the request by thefirst user further references an instruction to identify one or moreobjects or one or more users with respect to one or more of theactivities.
 4. The method of claim 1, wherein analyzing images of thescene of the video call to identify the one or more activities withinthe scene comprises: identifying user activity of one or more users ofthe plurality of client systems, wherein the identified user activity isidentified as being equivalent to one of the one or more activitiesreferenced in the request based on a taxonomy of activity classes. 5.The method of claim 1, further comprising: accessing, from the contextengine of the assistant system, context data associated with the videocall, wherein the context data indicates properties of a scene of thevideo call to identify one or more activities or objects within thescene.
 6. The method of claim 5, wherein the context data comprisesidentifications of one or more objects within the scene of the videocall, and wherein the request by the first user further references oneor more of the identified objects.
 7. The method of claim 5, wherein thecontext data comprises identifications of one or more users within thescene of the video call, and wherein the request by the first userfurther references one or more of the identified users.
 8. The method ofclaim 5, wherein the context data comprises location information of oneor more objects or one or more users within the scene of the video call,and wherein the request by the first user further references a locationof one or more of the objects or users.
 9. The method of claim 5,wherein the context data comprises information indicating one or morecontext changes with respect to one or more objects within the scene ofthe video call, and wherein the request by the first user furtherreferences context of one or more of the objects.
 10. The method ofclaim 5, wherein the context data comprises information indicatingcontext changes with respect to one or more users within the scene ofthe video call, and wherein the request by the first user furtherreferences context of one or more users.
 11. The method of claim 5,wherein the context data comprises information indicating contextchanges with respect to the location of one or more objects or one ormore users within the scene of the video call, and wherein the requestby the first user further references a location of one or more of theobjects or users.
 12. The method of claim 1, wherein the assistantsystem comprises a scene understanding engine.
 13. The method of claim12, further comprising: accessing, from the scene understanding engineof the assistant system, relationship data associated with the scene ofthe video call, wherein the relationship data indicates relationshipsbetween one or more users or one or more objects within the scene of thevideo call, wherein the request by the first user further references arelationship between one or more of the objects or users.
 14. The methodof claim 12, further comprising: determining the request by the firstuser further references a particular type of relationship data; andactivating the scene understanding engine in response to determining therequest references the particular type of relationship data, wherein thescene understanding engine analyzes the scene of the video call togenerate relationship data of the particular type of relationship datareferenced in the request.
 15. The method of claim 14, wherein the sceneunderstanding engine generates the relationship data in real time inresponse to being activated.
 16. The method of claim 14, furthercomprising: deactivating the scene understanding engine after therelationship data has been generated.
 17. The method of claim 12,wherein the scene understanding engine accesses the relationship data inresponse to a subsequent request by the first user to be performed bythe assistant system during the video call.
 18. The method of claim 12,further comprising: activating the scene understanding engine inresponse to determining the request references a particular type ofrelationship, wherein the scene understanding engine analyzes the sceneof the video call to generate relationship data of the particular typeof relationship referenced in the request; identifying one or morechanges between the particular type of relationship in the scene of thevideo call; and in response to determining the request referenceschanges between the particular type of relationship in the scene of thevideo call, instructing the assistant system to shift the scene of thevideo call.
 19. One or more computer-readable non-transitory storagemedia embodying software that is operable when executed to: establish avideo call between a plurality of client systems, wherein access to anassistant system is persistently maintained during the video call;receive, from a first client system of the plurality of client systems,a request by a first user to be performed by the assistant system duringthe video call, wherein the request references one or more activitiesassociated with one or more users associated with the plurality ofclient systems; analyze, by a context engine of the assistant system,images of a scene of the video call to identify the one or moreactivities within the scene; instruct the assistant system to executethe request based on the identified one or more activities; and send, toone or more of the plurality of client systems, a response to therequest while maintaining the video call between the plurality of clientsystems.
 20. A system comprising one or more processors and one or morecomputer-readable non-transitory storage media coupled to one or more ofthe processors and comprising instructions operable, when executed byone or more of the processors, to cause the system to: establish a videocall between a plurality of client systems, wherein access to anassistant system is persistently maintained during the video call;receive, from a first client system of the plurality of client systems,a request by a first user to be performed by the assistant system duringthe video call, wherein the request references one or more activitiesassociated with one or more users associated with the plurality ofclient systems; analyze, by a context engine of the assistant system,images of a scene of the video call to identify the one or moreactivities within the scene; instruct the assistant system to executethe request based on the identified one or more activities; and send, toone or more of the plurality of client systems, a response to therequest while maintaining the video call between the plurality of clientsystems.